Todays AI Summary

AI Developments: Resume Parsing, Efficient Language Models, and Poker-Playing AI

Today's AI landscape features advancements in diverse areas, from parsing resumes to creating more efficient language models and even mastering games like Liar's Poker. Here's a look at the key developments:

Research Highlights

  • Liar's Poker Mastery: A new paper introduces "Solly," an AI agent that has achieved elite human-level performance in Liar's Poker using self-play and reinforcement learning. Solly outperforms even large language models in this complex game of bluffing and strategy.
  • Understanding Misunderstandings: Research introduces a new annotation scheme for analyzing misunderstandings in collaborative dialogues, focusing on how different participants interpret references. This framework aims to improve the ability of language models to model perspective-dependent grounding.
  • Agentic LLM for Circuit Design: A novel agentic AI framework called "AnaFlow" uses multiple collaborating LLM-based agents to automate analog circuit sizing. This approach improves sample efficiency and explainability in circuit design.
  • OpenHands Software Agent SDK: The OpenHands Software Agent SDK is presented as a toolkit for building production-ready software engineering agents. It offers flexibility, security, and interfaces for user interaction, outperforming existing SDKs on benchmarks.
  • Structured Matrix Scaling for Calibration: A paper explores structured matrix scaling methods for multi-class calibration, aiming to improve the reliability of probability estimates from classifiers. The proposed methods show substantial gains over existing techniques.
  • Side-Channel Attack on LLMs: "Whisper Leak" is introduced as a side-channel attack that can infer user prompt topics from encrypted LLM traffic by analyzing packet size and timing patterns. This vulnerability poses privacy risks for users under network surveillance.
  • DQN Performance Analysis: A detailed study examines the impact of epsilon-greedy exploration schedules and prioritized experience replay on Deep Q-Networks (DQN) in finite environments. The findings offer practical recommendations for robust reinforcement learning.
  • Chinese Document QA Dataset: The Chinese Multi-Document Question Answering Dataset (ChiMDQA) is presented, designed for downstream business scenarios across various domains. It includes 6,068 high-quality QA pairs and a fine-grained evaluation system.
  • Explaining Human Choice Probabilities: Research explores how people make choices in stochastic environments, developing a model based on vector representations of choice frequency histograms. The model captures human choice patterns using simple operations.
  • Watermarking LLMs in Europe: A paper analyzes the requirements of the EU AI Act regarding watermarking large language models, mapping the criteria to state-of-the-art evaluations and comparing current watermarking methods.

Model Updates

  • SmartResume: Alibaba-EI releases SmartResume, a system for intelligent resume parsing. It supports various document formats, uses OCR and PDF metadata for text extraction, and leverages a fine-tuned Qwen3-0.6B model to convert content into structured fields.
  • aquif-3.5-Max-42B-A3B-GGUF: Unsloth releases a GGUF-quantized version of the aquif-3.5-Max model, part of the aquif-3.5 series. This model boasts advanced reasoning capabilities and a large context window, achieving high scores on benchmarks like MMLU-Pro and GPQA Diamond.
  • Kimi-Linear-REAP-35B-A3B-Instruct: Cerebras introduces a compressed variant of Kimi-Linear-48B-A3B-Instruct, achieved through Router-weighted Expert Activation Pruning (REAP). This model maintains near-identical performance while being 30% lighter.
  • Copywriting LLM: Kavyaah releases a copywriting LLM fine-tuned on Mistral-7B-Instruct for generating short, high-converting push notifications and ad copies.
  • Financial Sentiment Qwen3: YorkFr releases a fine-tuned version of Qwen3 for financial news sentiment classification.
  • Turkish Sentiment Analysis Finetuned: codealchemist01 releases a fine-tuned version of their Turkish sentiment analysis model, improved with additional balanced training data.
  • Formula OCR: karankulshrestha releases a model that performs OCR on handwritten calculus formulas.

Key Takeaways

  • Specialized Models Emerge: The trend of fine-tuning models for specific tasks continues, with models like SmartResume and the Copywriting LLM demonstrating the benefits of targeted training.
  • Efficiency is Key: Models like Kimi-Linear-REAP-35B-A3B-Instruct highlight the importance of model compression and efficient architectures for real-world deployment

AI Papers for 2026-02-27

Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets

The reliability of multilingual Large Language Model (LLM) evaluation is currently compromised by the inconsistent quality of translated benchmarks. Existing resources often suffer from semantic drift and context loss, which can lead to misleading performance metrics. In this work, we present a fully automated framework designed to address these challenges by enabling scalable, high-quality translation of datasets and benchmarks. We demonstrate that adapting test-time compute scaling strategies, specifically Universal Self-Improvement (USI) and our proposed multi-round ranking method, T-RANK, allows for significantly higher quality outputs compared to traditional pipelines. Our framework ensures that benchmarks preserve their original task structure and linguistic nuances during localization. We apply this approach to translate popular benchmarks and datasets into eight Eastern and Southern European languages (Ukrainian, Bulgarian, Slovak, Romanian, Lithuanian, Estonian, Turkish, Greek). Evaluations using both reference-based metrics and LLM-as-a-judge show that our translations surpass existing resources, resulting in more accurate downstream model assessment. We release both the framework and the improved benchmarks to facilitate robust and reproducible multilingual AI development.

Off-The-Shelf Image-to-Image Models Are All You Need To Defeat Image Protection Schemes

Advances in Generative AI (GenAI) have led to the development of various protection strategies to prevent the unauthorized use of images. These methods rely on adding imperceptible protective perturbations to images to thwart misuse such as style mimicry or deepfake manipulations. Although previous attacks on these protections required specialized, purpose-built methods, we demonstrate that this is no longer necessary. We show that off-the-shelf image-to-image GenAI models can be repurposed as generic ``denoisers" using a simple text prompt, effectively removing a wide range of protective perturbations. Across 8 case studies spanning 6 diverse protection schemes, our general-purpose attack not only circumvents these defenses but also outperforms existing specialized attacks while preserving the image's utility for the adversary. Our findings reveal a critical and widespread vulnerability in the current landscape of image protection, indicating that many schemes provide a false sense of security. We stress the urgent need to develop robust defenses and establish that any future protection mechanism must be benchmarked against attacks from off-the-shelf GenAI models. Code is available in this repository: https://github.com/mlsecviswanath/img2imgdenoiser

GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL

Open-source native GUI agents still lag behind closed-source systems on long-horizon navigation tasks. This gap stems from two limitations: a shortage of high-quality, action-aligned reasoning data, and the direct adoption of generic post-training pipelines that overlook the unique challenges of GUI agents. We identify two fundamental issues in these pipelines: (i) standard SFT with CoT reasoning often hurts grounding, and (ii) step-wise RLVR-tyle training faces partial verifiability, where multiple actions can be correct but only a single demonstrated action is used for verification. This makes offline step-wise metrics weak predictors of online task success. In this work, we present GUI-Libra, a tailored training recipe that addresses these challenges. First, to mitigate the scarcity of action-aligned reasoning data, we introduce a data construction and filtering pipeline and release a curated 81K GUI reasoning dataset. Second, to reconcile reasoning with grounding, we propose action-aware SFT that mixes reasoning-then-action and direct-action data and reweights tokens to emphasize action and grounding. Third, to stabilize RL under partial verifiability, we identify the overlooked importance of KL regularization in RLVR and show that a KL trust region is critical for improving offline-to-online predictability; we further introduce success-adaptive scaling to downweight unreliable negative gradients. Across diverse web and mobile benchmarks, GUI-Libra consistently improves both step-wise accuracy and end-to-end task completion. Our results suggest that carefully designed post-training and data curation can unlock significantly stronger task-solving capabilities without costly online data collection. We release our dataset, code, and models to facilitate further research on data-efficient post-training for reasoning-capable GUI agents.

Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach

Modelling rock-fluid interaction requires solving a set of partial differential equations (PDEs) to predict the flow behaviour and the reactions of the fluid with the rock on the interfaces. Conventional high-fidelity numerical models require a high resolution to obtain reliable results, resulting in huge computational expense. This restricts the applicability of these models for multi-query problems, such as uncertainty quantification and optimisation, which require running numerous scenarios. As a cheaper alternative to high-fidelity models, this work develops eight surrogate models for predicting the fluid flow in porous media. Four of these are reduced-order models (ROM) based on one neural network for compression and another for prediction. The other four are single neural networks with the property of grid-size invariance; a term which we use to refer to image-to-image models that are capable of inferring on computational domains that are larger than those used during training. In addition to the novel grid-size-invariant framework for surrogate models, we compare the predictive performance of UNet and UNet++ architectures, and demonstrate that UNet++ outperforms UNet for surrogate models. Furthermore, we show that the grid-size-invariant approach is a reliable way to reduce memory consumption during training, resulting in good correlation between predicted and ground-truth values and outperforming the ROMs analysed. The application analysed is particularly challenging because fluid-induced rock dissolution results in a non-static solid field and, consequently, it cannot be used to help in adjustments of the future prediction.

Enhancing Framingham Cardiovascular Risk Score Transparency through Logic-Based XAI

Cardiovascular disease (CVD) remains one of the leading global health challenges, accounting for more than 19 million deaths worldwide. To address this, several tools that aim to predict CVD risk and support clinical decision making have been developed. In particular, the Framingham Risk Score (FRS) is one of the most widely used and recommended worldwide. However, it does not explain why a patient was assigned to a particular risk category nor how it can be reduced. Due to this lack of transparency, we present a logical explainer for the FRS. Based on first-order logic and explainable artificial intelligence (XAI) fundaments, the explainer is capable of identifying a minimal set of patient attributes that are sufficient to explain a given risk classification. Our explainer also produces actionable scenarios that illustrate which modifiable variables would reduce a patient's risk category. We evaluated all possible input combinations of the FRS (over 22,000 samples) and tested them with our explainer, successfully identifying important risk factors and suggesting focused interventions for each case. The results may improve clinician trust and facilitate a wider implementation of CVD risk assessment by converting opaque scores into transparent and prescriptive insights, particularly in areas with restricted access to specialists.

Provable Last-Iterate Convergence for Multi-Objective Safe LLM Alignment via Optimistic Primal-Dual

Reinforcement Learning from Human Feedback (RLHF) plays a significant role in aligning Large Language Models (LLMs) with human preferences. While RLHF with expected reward constraints can be formulated as a primal-dual optimization problem, standard primal-dual methods only guarantee convergence with a distributional policy where the saddle-point problem is in convex-concave form. Moreover, standard primal-dual methods may exhibit instability or divergence in the last iterate under policy parameterization in practical applications. In this work, we propose a universal primal-dual framework for safe RLHF that unifies a broad class of existing alignment algorithms, including safe-RLHF, one-shot, and multi-shot based methods. Building on this framework, we introduce an optimistic primal-dual (OPD) algorithm that incorporates predictive updates for both primal and dual variables to stabilize saddle-point dynamics. We establish last-iterate convergence guarantees for the proposed method, covering both exact policy optimization in the distributional space and convergence to a neighborhood of the optimal solution whose gap is related to approximation error and bias under parameterized policies. Our analysis reveals that optimism plays a crucial role in mitigating oscillations inherent to constrained alignment objectives, thereby closing a key theoretical gap between constrained RL and practical RLHF.

When AI Writes, Whose Voice Remains? Quantifying Cultural Marker Erasure Across World English Varieties in Large Language Models

Large Language Models (LLMs) are increasingly used to ``professionalize'' workplace communication, often at the cost of linguistic identity. We introduce "Cultural Ghosting", the systematic erasure of linguistic markers unique to non-native English varieties during text processing. Through analysis of 22,350 LLM outputs generated from 1,490 culturally marked texts (Indian, Singaporean,& Nigerian English) processed by five models under three prompt conditions, we quantify this phenomenon using two novel metrics: Identity Erasure Rate (IER) & Semantic Preservation Score (SPS). Across all prompts, we find an overall IER of 10.26%, with model-level variation from 3.5% to 20.5% (5.9x range). Crucially, we identify a Semantic Preservation Paradox: models maintain high semantic similarity (mean SPS = 0.748) while systematically erasing cultural markers. Pragmatic markers (politeness conventions) are 1.9x more vulnerable than lexical markers (71.5% vs. 37.1% erasure). Our experiments demonstrate that explicit cultural-preservation prompts reduce erasure by 29% without sacrificing semantic quality.

NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors

Object hallucination is a critical issue in Large Vision-Language Models (LVLMs), where outputs include objects that do not appear in the input image. A natural question arises from this phenomenon: Which component of the LVLM pipeline primarily contributes to object hallucinations? The vision encoder to perceive visual information, or the language decoder to generate text responses? In this work, we strive to answer this question through designing a systematic experiment to analyze the roles of the vision encoder and the language decoder in hallucination generation. Our observations reveal that object hallucinations are predominantly associated with the strong priors from the language decoder. Based on this finding, we propose a simple and training-free framework, No-Language-Hallucination Decoding, NoLan, which refines the output distribution by dynamically suppressing language priors, modulated based on the output distribution difference between multimodal and text-only inputs. Experimental results demonstrate that NoLan effectively reduces object hallucinations across various LVLMs on different tasks. For instance, NoLan achieves substantial improvements on POPE, enhancing the accuracy of LLaVA-1.5 7B and Qwen-VL 7B by up to 6.45 and 7.21, respectively. The code is publicly available at: https://github.com/lingfengren/NoLan.

SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

Small language models (SLMs) offer compelling advantages in cost, latency, and adaptability, but have so far lagged behind larger models on long-horizon software engineering tasks such as SWE-bench, where they suffer from pervasive action looping and low resolution rates. We introduce SWE-Protégé, a post-training framework that reframes software repair as an expert-protégé collaboration problem. In SWE-Protégé, an SLM remains the sole decision-maker while learning to selectively seek guidance from a strong expert model, recognize stalled states, and follow through on expert feedback. Our approach combines supervised fine-tuning on expert-augmented trajectories with agentic reinforcement learning that explicitly discourages degenerative looping and unproductive expert collaboration. We lightly post-train Qwen2.5-Coder-7B-Instruct to achieve 42.4% Pass@1 on SWE-bench Verified, a +25.4% improvement over the prior SLM state of the art, while using expert assistance sparsely (~4 calls per task and 11% of total tokens).

Don't stop me now: Rethinking Validation Criteria for Model Parameter Selection

Despite the extensive literature on training loss functions, the evaluation of generalization on the validation set remains underexplored. In this work, we conduct a systematic empirical and statistical study of how the validation criterion used for model selection affects test performance in neural classifiers, with attention to early stopping. Using fully connected networks on standard benchmarks under $k$-fold evaluation, we compare: (i) early stopping with patience and (ii) post-hoc selection over all epochs (i.e. no early stopping). Models are trained with cross-entropy, C-Loss, or PolyLoss; the model parameter selection on the validation set is made using accuracy or one of the three loss functions, each considered independently. Three main findings emerge. (1) Early stopping based on validation accuracy performs worst, consistently selecting checkpoints with lower test accuracy than both loss-based early stopping and post-hoc selection. (2) Loss-based validation criteria yield comparable and more stable test accuracy. (3) Across datasets and folds, any single validation rule often underperforms the test-optimal checkpoint. Overall, the selected model typically achieves test-set performance statistically lower than the best performance across all epochs, regardless of the validation criterion. Our results suggest avoiding validation accuracy (in particular with early stopping) for parameter selection, favoring loss-based validation criteria.

AI Models

mradermacher/Qwen3.5-27B-heretic-GGUF

base_model: coder3101/Qwen3.5-27B-heretic language:

  • en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3.5-27B/blob/main/LICENSE mradermacher: readme_rev: 1 quantized_by: mradermacher tags:
  • heretic
  • uncensored
  • decensored
  • abliterated

About

<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: -->

static quants of https://huggingface.co/coder3101/Qwen3.5-27B-heretic

<!-- provided-files -->

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | GGUF | mmproj-Q8_0 | 0.7 | multi-modal supplement | | GGUF | mmproj-f16 | 1.0 | multi-modal supplement | | GGUF | Q2_K | 10.2 | | | GGUF | Q3_K_S | 12.2 | | | GGUF | Q3_K_M | 13.4 | lower quality | | GGUF | Q3_K_L | 14.1 | | | GGUF | IQ4_XS | 14.9 | | | GGUF | Q4_K_S | 15.7 | fast, recommended | | GGUF | Q4_K_M | 16.6 | fast, recommended | | GGUF | Q5_K_S | 18.8 | | | GGUF | Q5_K_M | 19.5 | | | GGUF | Q6_K | 22.2 | very good quality | | GGUF | Q8_0 | 28.7 | fast, best quality |

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

image.png

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

<!-- end -->

Author: mradermacher

Likes: 9

Downloads: 0

Tags: transformers, gguf, heretic, uncensored, decensored, abliterated, en, base_model:coder3101/Qwen3.5-27B-heretic, base_model:quantized:coder3101/Qwen3.5-27B-heretic, license:apache-2.0, endpoints_compatible, region:us, conversational

mradermacher/Qwen3.5-35B-A3B-heretic-GGUF

base_model: brayniac/Qwen3.5-35B-A3B-heretic language:

  • en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3.5-35B-A3B/blob/main/LICENSE mradermacher: readme_rev: 1 quantized_by: mradermacher tags:
  • heretic
  • uncensored
  • decensored
  • abliterated

About

<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: 1 -->

static quants of https://huggingface.co/brayniac/Qwen3.5-35B-A3B-heretic

<!-- provided-files -->

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | GGUF | Q2_K | 12.9 | | | GGUF | Q3_K_S | 15.3 | | | GGUF | Q3_K_M | 16.9 | lower quality | | GGUF | Q3_K_L | 18.2 | | | GGUF | IQ4_XS | 19.0 | | | GGUF | Q4_K_S | 20.0 | fast, recommended | | GGUF | Q4_K_M | 21.3 | fast, recommended | | GGUF | Q5_K_S | 24.1 | | | GGUF | Q5_K_M | 24.9 | | | GGUF | Q6_K | 28.6 | very good quality | | GGUF | Q8_0 | 37.0 | fast, best quality |

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

image.png

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

<!-- end -->

Author: mradermacher

Likes: 5

Downloads: 0

Tags: transformers, gguf, heretic, uncensored, decensored, abliterated, en, base_model:brayniac/Qwen3.5-35B-A3B-heretic, base_model:quantized:brayniac/Qwen3.5-35B-A3B-heretic, license:apache-2.0, endpoints_compatible, region:us, conversational

alexdenton/Qwen3.5-35B-A3B-heretic-GGUF

base_model: brayniac/Qwen3.5-35B-A3B-heretic quantized_by: alexdenton tags:

  • gguf
  • qwen3.5
  • moe
  • llama-cpp
  • quantized library_name: gguf license: apache-2.0

Qwen3.5-35B-A3B-heretic GGUF

GGUF quantizations of brayniac/Qwen3.5-35B-A3B-heretic.

This is a Mixture of Experts (MoE) model with 35B total parameters and ~3B active parameters per token.

Available quantizations

| File | Quant | Size | BPW | Quality | |------|-------|------|-----|---------| | Qwen3.5-35B-A3B-heretic-Q8_0.gguf | Q8_0 | 34.4 GB | 8.52 | Excellent — near-lossless | | Qwen3.5-35B-A3B-heretic-Q6_K.gguf | Q6_K | 26.5 GB | 6.58 | Very good | | Qwen3.5-35B-A3B-heretic-Q5_K_M.gguf | Q5_K_M | 23 GB | 5.71 | Good | | Qwen3.5-35B-A3B-heretic-Q4_K_M.gguf | Q4_K_M | 19.7 GB | 4.88 | Recommended for most users | | Qwen3.5-35B-A3B-heretic-Q3_K_M.gguf | Q3_K_M | 15.6 GB | 3.87 | Usable — for low VRAM setups |

Multimodal projector (vision)

| File | Format | Size | |------|--------|------| | Qwen3.5-35B-A3B-mmproj-F16.gguf | F16 | ~889 MB |

How to use

llama-server (recommended for API usage)

llama-server \
    -m Qwen3.5-35B-A3B-heretic-Q8_0.gguf \
    --mmproj Qwen3.5-35B-A3B-mmproj-F16.gguf \
    --host 0.0.0.0 \
    --ctx-size 32768 \
    --gpu-layers 99 \
    --tensor-split 1,1 \
    --chat-template-kwargs '{"enable_thinking": false}' \
    --repeat-penalty 1.0 \
    --presence-penalty 1.5 \
    --min-p 0.0 \
    --top-k 20 \
    --top-p 0.8 \
    --temp 0.7

llama-cli

llama-cli -m Qwen3.5-35B-A3B-heretic-Q4_K_M.gguf -ngl 99 -c 4096

Which quantization should I use?

  • Q8_0: Best quality, needs ~35 GB VRAM. Fits on 2x RTX 3090.
  • Q6_K: Great quality, needs ~27 GB VRAM. Fits on 2x RTX 3090 with room for context.
  • Q5_K_M: Good balance. Fits on 1x RTX 3090 (24 GB).
  • Q4_K_M: Most popular choice. Fits on 1x RTX 3090 with context room.
  • Q3_K_M: For constrained VRAM setups (~16 GB needed).

Quantization details

Author: alexdenton

Likes: 5

Downloads: 4

Tags: gguf, qwen3.5, moe, llama-cpp, quantized, base_model:brayniac/Qwen3.5-35B-A3B-heretic, base_model:quantized:brayniac/Qwen3.5-35B-A3B-heretic, license:apache-2.0, endpoints_compatible, region:us, conversational

tvall43/Qwen3.5-35B-A3B-heretic-mxfp4-gguf

library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3.5-35B-A3B/blob/main/LICENSE pipeline_tag: image-text-to-text tags:

  • heretic
  • uncensored
  • decensored
  • abliterated

mxfp4 quant of https://huggingface.co/brayniac/Qwen3.5-35B-A3B-heretic

This is a decensored version of a model, made using Heretic v1.2.0

Abliteration parameters

| Parameter | Value | | :-------- | :---: | | direction_index | 20.06 | | linear_attn.out_proj.max_weight | 0.93 | | linear_attn.out_proj.max_weight_position | 37.63 | | linear_attn.out_proj.min_weight | 0.71 | | linear_attn.out_proj.min_weight_distance | 4.94 | | moe_experts.down_proj.max_weight | 1.18 | | moe_experts.down_proj.max_weight_position | 23.68 | | moe_experts.down_proj.min_weight | 0.86 | | moe_experts.down_proj.min_weight_distance | 5.48 | | shared_expert.down_proj.max_weight | 1.33 | | shared_expert.down_proj.max_weight_position | 36.08 | | shared_expert.down_proj.min_weight | 0.70 | | shared_expert.down_proj.min_weight_distance | 17.75 | | attn.o_proj.max_weight | 0.83 | | attn.o_proj.max_weight_position | 35.00 | | attn.o_proj.min_weight | 0.06 | | attn.o_proj.min_weight_distance | 15.22 |

Performance

| Metric | This model | Original model (a model) | | :----- | :--------: | :---------------------------: | | KL divergence | 0.0825 | 0 (by definition) | | Refusals | 5/100 | 92/100 |

Qwen3.5-35B-A3B

<img width="400px" src="https://qianwen-res.oss-accelerate.aliyuncs.com/logo_qwen3.5.png">

Qwen Chat

[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.

These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.

[!Tip] For users seeking managed, scalable inference without infrastructure maintenance, the official Qwen API service is provided by Alibaba Cloud Model Studio.

In particular, Qwen3.5-Flash is the hosted version corresponding to Qwen3.5-35B-A3B with more production features, e.g., 1M context length by default and official built-in tools. For more information, please refer to the User Guide.

Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.

Qwen3.5 Highlights

Qwen3.5 features the following enhancement:

  • Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.

  • Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.

  • Scalable RL Generalization: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.

  • Global Linguistic Coverage: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.

  • Next-Generation Training Infrastructure: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration.

Benchmark Results

For more details, please refer to our blog post Qwen3.5.

Model Overview

  • Type: Causal Language Model with Vision Encoder
  • Training Stage: Pre-training & Post-training
  • Language Model
    • Number of Parameters: 35B in total and 3B activated
    • Hidden Dimension: 2048
    • Token Embedding: 248320 (Padded)
    • Number of Layers: 40
    • Hidden Layout: 10 × (3 × (Gated DeltaNet → MoE) → 1 × (Gated Attention → MoE))
    • Gated DeltaNet:
      • Number of Linear Attention Heads: 32 for V and 16 for QK
      • Head Dimension: 128
    • Gated Attention:
      • Number of Attention Heads: 16 for Q and 2 for KV
      • Head Dimension: 256
      • Rotary Position Embedding Dimension: 64
    • Mixture Of Experts
      • Number of Experts: 256
      • Number of Activated Experts: 8 Routed + 1 Shared
      • Expert Intermediate Dimension: 512
    • LM Output: 248320 (Padded)
    • MTP: trained with multi-steps
  • Context Length: 262,144 natively and extensible up to 1,010,000 tokens.

Benchmark Results

Language

<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;max-width:1000px;margin:0 auto;padding:16px 0"> <table style="width:100%;border-collapse:collapse;font-size:13px"> <thead><tr> <th style="padding:10px 7px;text-align:left;font-weight:600;border-bottom:2px solid #7c3aed;color:#7c3aed"></th> <th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">GPT-5-mini 2025-08-07</th> <th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">GPT-OSS-120B</th> <th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3-235B-A22B</th> <th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3.5-122B-A10B</th> <th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3.5-27B</th> <th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3.5-35B-A3B</th> </tr></thead> <tbody> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Knowledge</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMLU-Pro</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.3</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMLU-Redux</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">94.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.3</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">C-Eval</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.2</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SuperGPQA</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">58.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">54.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">64.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.4</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Instruction Following</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">IFEval</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">95.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.9</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">IFBench</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">51.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.2</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MultiChallenge</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">59.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">45.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">50.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.0</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Long Context</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">AA-LCR</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">50.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">66.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">66.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">58.5</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">LongBench v2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">56.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">48.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">54.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">59.0</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">STEM & Reasoning</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">HLE w/ CoT</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">19.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">14.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">18.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">25.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">24.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">22.4</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">GPQA Diamond</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.2</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">HMMT Feb 25</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.0</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">HMMT Nov 25</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.2</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Coding</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">72.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">72.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">72.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.2</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Terminal Bench 2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">31.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">18.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">49.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">40.5</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">LiveCodeBench v6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.6</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">CodeForces</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">2160</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">2157</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">2146</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">2100</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">1899</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">2028</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">OJBench</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">40.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">32.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">39.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">40.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">36.0</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">FullStackBench en</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">30.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">58.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">58.1</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">FullStackBench zh</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">35.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">58.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">57.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">55.0</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">General Agent</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">BFCL-V4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">55.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">54.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">72.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.3</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">TAU2-Bench</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">58.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.2</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">VITA-Bench</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">13.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">31.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">33.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">31.9</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">DeepPlanning</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">17.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">17.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">24.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">22.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">22.8</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Search Agent</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">HLE w/ tool</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">35.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">19.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">47.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">48.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">47.4</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Browsecomp</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">48.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.0</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Browsecomp-zh</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">49.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">42.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.5</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">WideSearch</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">47.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">40.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">57.1</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Seal-0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">34.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">45.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">44.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">47.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.4</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Multilingualism</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMMLU</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.2</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMLU-ProX</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.0</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">NOVA-63</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">51.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">51.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">55.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">58.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">58.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">57.1</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">INCLUDE</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.7</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Global PIQA</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.6</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">PolyMATH</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">54.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">71.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">64.4</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">WMT24++</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.3</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MAXIFE</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.6</td> </tr> </tbody> </table> <p style="margin-top:12px;font-size:11px;opacity:0.7"> * CodeForces: evaluated on our own query set.<br> * TAU2-Bench: we follow the official setup except for the airline domain, where all models are evaluated by applying the fixes proposed in the Claude Opus 4.5 system card.<br> * Search Agent: most search agents built on our model adopt a simple context-folding strategy(256k): once the cumulative Tool Response length reaches a preset threshold, earlier Tool Responses are pruned from the history to keep the context within limits.<br> * WideSearch: we use a 256k context window without any context management.<br> * MMLU-ProX: we report the averaged accuracy on 29 languages.<br> * WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL.<br> * MAXIFE: we report the accuracy on English + multilingual original prompts (totally 23 settings).<br> * Empty cells (--) indicate scores not yet available or not applicable. </p> </div>

Vision Language

<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;max-width:900px;margin:0 auto;padding:16px 0"> <table style="width:100%;border-collapse:collapse;font-size:13px"> <thead><tr> <th style="padding:10px 7px;text-align:left;font-weight:600;border-bottom:2px solid #7c3aed;color:#7c3aed"></th> <th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">GPT-5-mini 2025-08-07</th> <th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Claude-Sonnet-4.5</th> <th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3-VL-235B-A22B</th> <th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3.5-122B-A10B</th> <th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3.5-27B</th> <th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3.5-35B-A3B</th> </tr></thead> <tbody> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">STEM and Puzzle</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMMU</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.4</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMMU-Pro</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.1</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MathVision</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">71.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">71.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.9</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Mathvista(mini)</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.2</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">DynaMath</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.0</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">ZEROBench</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">10</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">8</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">ZEROBench_sub</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">27.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">26.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">28.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">36.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">36.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">34.1</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">VlmsAreBlind</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">96.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">96.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">97.0</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">BabyVision</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">20.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">18.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">22.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">40.2 / 34.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">44.6 / 34.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">38.4 / 29.6</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">General VQA</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">RealWorldQA</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.1</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMStar</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.9</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMBench<sub><small>EN-DEV-v1.1</small></sub></td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.5</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SimpleVQA</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">56.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">57.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">56.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">58.3</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">HallusionBench</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">59.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">66.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.9</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Text Recognition and Document Understanding</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">OmniDocBench1.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.3</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">CharXiv(RQ)</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">66.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.5</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMLongBench-Doc</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">50.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">56.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">59.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">59.5</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">CC-OCR</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.7</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">AI2D_TEST</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.6</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">OCRBench</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.0</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Spatial Intelligence</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">ERQA</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">54.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">45.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">52.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">64.8</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">CountBench</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">97.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">97.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">97.8</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">RefCOCO(avg)</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.2</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">ODInW13</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">43.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">44.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">42.6</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">EmbSpatialBench</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">71.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.1</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">RefSpatialBench</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">9.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">2.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.5</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">LingoQA</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">12.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">66.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.2</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Hypersim</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">11.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">12.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">13.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">13.1</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SUNRGBD</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">34.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">36.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">35.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">33.4</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Nuscene</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">13.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">15.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">15.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">14.6</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Video Understanding</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">VideoMME<sub><small>(w sub.)</sub></small></td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.6</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">VideoMME<sub><small>(w/o sub.)</sub></small></td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.5</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">VideoMMMU</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.4</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MLVU</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">72.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.6</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MVBench</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.8</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">LVBench</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">71.4</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMVU</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">71.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">72.3</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Visual Agent</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">ScreenSpot Pro</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">36.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.6</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">OSWorld-Verified</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">38.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">58.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">56.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">54.5</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">AndroidWorld</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">66.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">64.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">71.1</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Tool Calling</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">TIR-Bench</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">24.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">27.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">29.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">53.2 / 42.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">59.8 / 42.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">55.5 / 38.0</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">V*</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">71.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">58.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.2 / 90.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.7 / 89.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.7 / 89.5</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Medical VQA</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SLAKE</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">54.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.7</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">PMC-VQA</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">36.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">55.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.0</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MedXpertQA-MM</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">34.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">54.0</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">47.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.4</td> </tr> </tbody> </table> <p style="margin-top:12px;font-size:11px;opacity:0.7"> * MathVision: our model’s score is evaluated using a fixed prompt, e.g., “Please reason step by step, and put your final answer within \boxed{}.” For other models, we report the higher score between runs with and without the \boxed{} formatting.<br> * BabyVision: scores reported as "with CI / without CI".<br> * TIR-Bench and V*: scores reported as "with CI / without CI".<br> * Empty cells (--) indicate scores not yet available or not applicable. </p> </div>

Quickstart

[!Important] Qwen3.5 models operate in thinking mode by default, generating thinking content signified by <think>\n...</think>\n\n before producing the final responses. To disable thinking content and obtain direct response, refer to the examples here.

For streamlined integration, we recommend using Qwen3.5 via APIs. Below is a guide to use Qwen3.5 via OpenAI-compatible API.

Serving Qwen3.5

Qwen3.5 can be served via APIs with popular inference frameworks. In the following, we show example commands to launch OpenAI-Compatible API servers for Qwen3.5 models.

[!Important] Inference efficiency and throughput vary significantly across frameworks. We recommend using the latest framework versions to ensure optimal performance and compatibility. For production workloads or high-throughput scenarios, dedicated serving engines such as SGLang, KTransformers or vLLM are strongly recommended.

[!Important] The model has a default context length of 262,144 tokens. If you encounter out-of-memory (OOM) errors, consider reducing the context window. However, because Qwen3.5 leverages extended context for complex tasks, we advise maintaining a context length of at least 128K tokens to preserve thinking capabilities.

SGLang

SGLang is a fast serving framework for large language models and vision language models. SGLang from the main branch of the open-source repository is required for Qwen3.5, which can be installed using the following command in a fresh environment:

uv pip install 'git+https://github.com/sgl-project/sglang.git#subdirectory=python&egg=sglang[all]'

See its documentation for more details.

The following will create API endpoints at http://localhost:8000/v1:

  • Standard Version: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs.

    python -m sglang.launch_server --model-path Qwen/Qwen3.5-35B-A3B --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3

  • Tool Use: To support tool use, you can use the following command.

    python -m sglang.launch_server --model-path Qwen/Qwen3.5-35B-A3B --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --tool-call-parser qwen3_coder

  • Multi-Token Prediction (MTP): The following command is recommended for MTP:

    python -m sglang.launch_server --model-path Qwen/Qwen3.5-35B-A3B --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --speculative-algo NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4

vLLM

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. vLLM from the main branch of the open-source repository is required for Qwen3.5, which can be installed using the following command in a fresh environment:

uv pip install vllm --torch-backend=auto --extra-index-url https://wheels.vllm.ai/nightly

See its documentation for more details.

For detailed Qwen3.5 usage guide, see the vLLM Qwen3.5 recipe.

The following will create API endpoints at http://localhost:8000/v1:

  • Standard Version: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs.

    vllm serve Qwen/Qwen3.5-35B-A3B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3

  • Tool Call: To support tool use, you can use the following command.

    vllm serve Qwen/Qwen3.5-35B-A3B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder

  • Multi-Token Prediction (MTP): The following command is recommended for MTP:

    vllm serve Qwen/Qwen3.5-35B-A3B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'

  • Text-Only: The following command skips the vision encoder and multimodal profiling to free up memory for additional KV cache:

    vllm serve Qwen/Qwen3.5-35B-A3B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --language-model-only

KTransformers

KTransformers is a flexible framework for experiencing cutting-edge LLM inference optimizations with CPU-GPU heterogeneous computing. For running Qwen3.5 with KTransformers, see the KTransformers Deployment Guide.

Hugging Face Transformers

Hugging Face Transformers contains a lightweight server which can be used for quick testing and moderate load deployment. The latest transformers is required for Qwen3.5:

pip install "transformers[serving] @ git+https://github.com/huggingface/transformers.git@main"

See its documentation for more details. Please also make sure torchvision and pillow are installed.

Then, run transformers serve to launch a server with API endpoints at http://localhost:8000/v1; it will place the model on accelerators if available:

transformers serve --force-model Qwen/Qwen3.5-35B-A3B --port 8000 --continuous-batching

Using Qwen3.5 via the Chat Completions API

The chat completions API is accessible via standard HTTP requests or OpenAI SDKs. Here, we show examples using the OpenAI Python SDK.

Before starting, make sure it is installed and the API key and the API base URL is configured, e.g.:

pip install -U openai

# Set the following accordingly
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"

[!Tip] We recommend using the following set of sampling parameters for generation

  • Thinking mode for general tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
  • Thinking mode for precise coding tasks (e.g. WebDev): temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
  • Instruct (or non-thinking) mode for general tasks: temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
  • Instruct (or non-thinking) mode for reasoning tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0

Please note that the support for sampling parameters varies according to inference frameworks.

Text-Only Input

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {"role": "user", "content": "Type \"I love Qwen3.5\" backwards"},
]

chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.5-35B-A3B",
    messages=messages,
    max_tokens=81920,
    temperature=1.0,
    top_p=0.95,
    presence_penalty=1.5,
    extra_body={
        "top_k": 20,
    }, 
)
print("Chat response:", chat_response)

Image Input

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/CI_Demo/mathv-1327.jpg"
                }
            },
            {
                "type": "text",
                "text": "The centres of the four illustrated circles are in the corners of the square. The two big circles touch each other and also the two little circles. With which factor do you have to multiply the radii of the little circles to obtain the radius of the big circles?\nChoices:\n(A) $\\frac{2}{9}$\n(B) $\\sqrt{5}$\n(C) $0.8 \\cdot \\pi$\n(D) 2.5\n(E) $1+\\sqrt{2}$"
            }
        ]
    }
]

response = client.chat.completions.create(
    model="Qwen/Qwen3.5-35B-A3B",
    messages=messages,
    max_tokens=81920,
    temperature=1.0,
    top_p=0.95,
    presence_penalty=1.5,
    extra_body={
        "top_k": 20,
    }, 
)
print("Chat response:", chat_response)

Video Input

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "video_url",
                "video_url": {
                    "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/video/N1cdUjctpG8.mp4"
                }
            },
            {
                "type": "text",
                "text": "How many porcelain jars were discovered in the niches located in the primary chamber of the tomb?"
            }
        ]
    }
]

# When vLLM is launched with `--media-io-kwargs '{"video": {"num_frames": -1}}'`,
# video frame sampling can be configured via `extra_body` (e.g., by setting `fps`).
# This feature is currently supported only in vLLM.
#
# By default, `fps=2` and `do_sample_frames=True`.
# With `do_sample_frames=True`, you can customize the `fps` value to set your desired video sampling rate.
response = client.chat.completions.create(
    model="Qwen/Qwen3.5-35B-A3B",
    messages=messages,
    max_tokens=81920,
    temperature=1.0,
    top_p=0.95,
    presence_penalty=1.5,
    extra_body={
        "top_k": 20,
        "mm_processor_kwargs": {"fps": 2, "do_sample_frames": True},
    }, 
)

print("Chat response:", chat_response)

Instruct (or Non-Thinking) Mode

[!Important] Qwen3.5 does not officially support the soft switch of Qwen3, i.e., /think and /nothink.

Qwen3.5 will think by default before response. You can obtain direct response from the model without thinking by configuring the API parameters. For example,

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/RealWorld/RealWorld-04.png"
                }
            },
            {
                "type": "text",
                "text": "Where is this?"
            }
        ]
    }
]

chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.5-35B-A3B",
    messages=messages,
    max_tokens=32768,
    temperature=0.7,
    top_p=0.8,
    presence_penalty=1.5,
    extra_body={
        "top_k": 20,
        "chat_template_kwargs": {"enable_thinking": False},
    }, 
)
print("Chat response:", chat_response)

[!Note] If you are using APIs from Alibaba Cloud Model Studio, in addition to changing model, please use "enable_thinking": False instead of "chat_template_kwargs": {"enable_thinking": False}.

Agentic Usage

Qwen3.5 excels in tool calling capabilities.

Qwen-Agent

We recommend using Qwen-Agent to quickly build Agent applications with Qwen3.5.

To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.

import os
from qwen_agent.agents import Assistant

# Define LLM
# Using Alibaba Cloud Model Studio
llm_cfg = {
    # Use the OpenAI-compatible model service provided by DashScope:
    'model': 'Qwen3.5-35B-A3B',
    'model_type': 'qwenvl_oai',
    'model_server': 'https://dashscope.aliyuncs.com/compatible-mode/v1',
    'api_key': os.getenv('DASHSCOPE_API_KEY'),

    'generate_cfg': {
        'use_raw_api': True,
        # When using Dash Scope OAI API, pass the parameter of whether to enable thinking mode in this way
        'extra_body': {
            'enable_thinking': True
        },
    },
}

# Using OpenAI-compatible API endpoint.
# functionality of the deployment frameworks and let Qwen-Agent automate the related operations.
#
# llm_cfg = {
#     # Use your own model service compatible with OpenAI API by vLLM/SGLang:
#     'model': 'Qwen/Qwen3.5-35B-A3B',
#     'model_type': 'qwenvl_oai',
#     'model_server': 'http://localhost:8000/v1',  # api_base
#     'api_key': 'EMPTY',
#
#     'generate_cfg': {
#         'use_raw_api': True,
#         # When using vLLM/SGLang OAI API, pass the parameter of whether to enable thinking mode in this way
#         'extra_body': {
#             'chat_template_kwargs': {'enable_thinking': True}
#         },
#     },
# }

# Define Tools
tools = [
    {'mcpServers': {  # You can specify the MCP configuration file
            "filesystem": {
                "command": "npx",
                "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/xxxx/Desktop"]
            }
        }
    }
]

# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)

# Streaming generation
messages = [{'role': 'user', 'content': 'Help me organize my desktop.'}]
for responses in bot.run(messages=messages):
    pass
print(responses)

# Streaming generation
messages = [{'role': 'user', 'content': 'Develop a dog website and save it on the desktop'}]
for responses in bot.run(messages=messages):
    pass
print(responses)

Qwen Code

Qwen Code is an open-source AI agent for the terminal, optimized for Qwen models. It helps you understand large codebases, automate tedious work, and ship faster.

For more information, please refer to Qwen Code.

Processing Ultra-Long Texts

Qwen3.5 natively supports context lengths of up to 262,144 tokens. For long-horizon tasks where the total length (including both input and output) exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively., e.g., YaRN.

YaRN is currently supported by several inference frameworks, e.g., transformers, vllm, ktransformers and sglang. In general, there are two approaches to enabling YaRN for supported frameworks:

  • Modifying the model configuration file: In the config.json file, change the rope_parameters fields in text_config to:

    {
    "mrope_interleaved": true,
    "mrope_section": [
        11,
        11,
        10
    ],
    "rope_type": "yarn",
    "rope_theta": 10000000,
    "partial_rotary_factor": 0.25,
    "factor": 4.0,
    "original_max_position_embeddings": 262144,
}

  • Passing command line arguments:

    For vllm, you can use

    VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve ... --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --max-model-len 1010000

    For sglang and ktransformers, you can use

    SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server ... --json-model-override-args '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --context-length 1010000

[!NOTE] All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise modifying the rope_parameters configuration only when processing long contexts is required. It is also recommended to modify the factor as needed. For example, if the typical context length for your application is 524,288 tokens, it would be better to set factor as 2.0.

Best Practices

To achieve optimal performance, we recommend the following settings:

  1. Sampling Parameters:

    • We suggest using the following sets of sampling parameters depending on the mode and task type:
      • Thinking mode for general tasks:
        temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
      • Thinking mode for precise coding tasks (e.g., WebDev):
        temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
      • Instruct (or non-thinking) mode for general tasks:
        temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
      • Instruct (or non-thinking) mode for reasoning tasks:
        temperature=1.0, top_p=1.0, top_k=40, min_p=0.0, presence_penalty=2.0, repetition_penalty=1.0
    • For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.

  2. Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 81,920 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.

  3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.

    • Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
    • Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g., "answer": "C"."

  4. No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.

  5. Long Video Understanding: To optimize inference efficiency for plain text and images, the size parameter in the released video_preprocessor_config.json is conservatively configured. It is recommended to set the longest_edge parameter in the video_preprocessor_config file to 469,762,048 (corresponding to 224k video tokens) to enable higher frame-rate sampling for hour-scale videos and thereby achieve superior performance. For example,

    {"longest_edge": 469762048, "shortest_edge": 4096}

    Alternatively, override the default values via engine startup parameters. For implementation details, refer to: vLLM / SGLang.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen3.5,
    title  = {{Qwen3.5}: Towards Native Multimodal Agents},
    author = {{Qwen Team}},
    month  = {February},
    year   = {2026},
    url    = {https://qwen.ai/blog?id=qwen3.5}
}

Author: tvall43

Likes: 4

Downloads: 651

Tags: transformers, safetensors, gguf, qwen3_5_moe, image-text-to-text, heretic, uncensored, decensored, abliterated, license:apache-2.0, endpoints_compatible, region:us

Comfy-Org/SDPose

license: mit

Author: Comfy-Org

Likes: 3

Downloads: 0

Tags: license:mit, region:us

OpenMOSE/Qwen3.5-REAP-97B-A10B

license: apache-2.0 base_model:

  • Qwen/Qwen3.5-122B-A10B

OpenMOSE/Qwen3.5-REAP-97B-A10B

Vision–Language MoE model created by applying Router-weighted Expert Activation Pruning (REAP) to Qwen3.5-122B-A10B.

1. Model Summary

  • Base model: Qwen/Qwen3.5-122B-A10B (vision–language MoE LLM)
  • Variant name: Qwen3.5-REAP-97B-A10B
  • Architecture: Decoder-only Transformer (hybrid linear/full attention) + MoE MLP experts, with vision encoder + VL fusion as in Qwen3.5
  • Pruning method: REAP (Router-weighted Expert Activation Pruning) by Cerebras Research https://github.com/CerebrasResearch/reap
  • Expert sparsity: ~22% of MoE experts pruned globally (256 → 200 experts)
  • Active parameters: "A10B" indicates roughly ~10B active parameters per token (MoE sparse activation, 8 experts per token), while total parameters are reduced to about 97B
  • Modality: Text + Vision (VL support kept intact)
  • License: Apache 2.0
  • Author / Maintainer: OpenMOSE
  • Year: 2025

This is an unofficial community variant of Qwen3.5, not affiliated with or endorsed by Alibaba or Cerebras Systems.

2. What Is REAP and What Did We Change?

REAP (Router-weighted Expert Activation Pruning) is a pruning method for MoE models that uses:

  • Router statistics (routing probabilities)
  • Expert activation patterns on a calibration set

to identify under-used or redundant experts and prune them while preserving model quality as much as possible.

For this model:

  • We applied REAP to Qwen3.5-122B-A10B across its MoE MLP blocks.
  • ~22% of experts are pruned (256 → 200), based on router-weighted activation statistics.
  • The routing mechanism itself is not conceptually changed; we only changed which experts remain.
  • We extended the original REAP implementation to support the Qwen3.5 hybrid architecture (interleaved linear attention + full attention layers), so pruning can be applied without disrupting either attention pathway or VL functionality.

In short: same REAP algorithm, adapted to Qwen3.5's hybrid linear/full attention MoE architecture, leaving VL functionality available.

3. Calibration Data

The REAP pruning statistics were computed using:

The calibration set is not used for additional fine-tuning; it is used solely to measure router/expert activations to decide which experts to prune.

4. Why 97B-A10B? (Motivation & Hardware Footprint)

Qwen3.5-122B-A10B is one of the most frontier-capable open-source models runnable at 96 GB, but it still exceeds what a typical 48 GB GPU setup can handle. By pruning ~22% of experts:

  • The model shrinks from ~122B total parameters to about 97B total parameters.

  • Sparse MoE activation keeps around 10B parameters active per token ("A10B"), same effective compute profile as the base model.

  • In practice, this makes the model feasible to deploy on a 48 GB GPU with modest CPU offload:

    • Aggressive quantization (e.g., Q4) eliminates the need for offloading on 96 GB setups.
    • 48 GB configurations can work with partial layer offloading to CPU.

The overarching goal is to bring the closest OSS approximation to a frontier model into local deployment, making Qwen3.5-122B-A10B accessible without requiring multi-GPU nodes.

5. Architecture Notes

Key architectural properties inherited from the base model and preserved after pruning:

| Property | Value | |---|---| | Hidden size | 3072 | | Num layers | 48 | | Attention type | Hybrid (3× linear + 1× full, repeating) | | Full attention interval | every 4th layer | | Num attention heads | 32 | | Num KV heads | 2 | | Head dim | 256 | | MoE experts total | 200 (pruned from 256) | | Experts per token | 8 | | MoE intermediate size | 1024 | | Max context length | 262,144 tokens | | Vocab size | 248,320 |

The hybrid attention design (linear attention layers interleaved with full attention every 4 layers) is a distinctive feature of the Qwen3.5 family and is retained fully after pruning.

6. Intended Use

Primary intended uses

  • Research on:

    • MoE pruning and compression (especially REAP) applied to hybrid attention architectures
    • Scaling behavior of pruned MoE VL models under conservative pruning ratios (~22%)
    • Trade-offs between expert sparsity and performance in linear-attention hybrids

  • Experimental deployment for:

    • Vision–language assistants on constrained hardware
    • Multimodal chatbots
    • Document + image understanding

Suitable tasks (examples)

  • Multimodal chat (image + text → text)
  • Image captioning / description
  • Visual question answering
  • General instruction-following and long-form text generation
  • Long-context reasoning (up to 262K tokens)

Out-of-scope / high-risk uses

This model should not be used without additional safeguards for:

  • Medical, legal, or financial advice
  • Safety-critical decision making
  • Political persuasion or targeted disinformation
  • Any scenario where incorrect or biased outputs can cause real-world harm

7. Limitations & Risks

This model inherits all the limitations of Qwen3.5-122B-A10B plus those introduced by pruning:

  • Hallucinations: The model can generate plausible but incorrect facts.

  • Bias & toxicity: Biases from the original training data and synthetic calibration data remain and may be amplified.

  • Distribution shift from pruning:

    • Some long-tail behaviors or rare domain knowledge may degrade due to removal of 56 experts.
    • Performance may be uneven across tasks or languages underrepresented in the calibration set.

  • Multimodal edge cases:

    • Complex compositional visual reasoning or high-resolution images may not work reliably.
    • VL behavior is preserved but not re-tuned after pruning.

  • Hybrid attention sensitivity:

    • Linear attention layers are more sensitive to expert distribution changes than standard full attention; this is a known risk with the Qwen3.5 architecture.

Users should perform their own evaluation before relying on the model in any sensitive context.

8. How to Use

Note: Requires transformers >= 4.57.0.dev0 for Qwen3.5 MoE support (Qwen3_5MoeForConditionalGeneration).

import torch
from transformers import AutoProcessor
from transformers.models.qwen3_5_moe import Qwen3_5MoeForConditionalGeneration

model_id = "OpenMOSE/Qwen3.5-REAP-97B-A10B"


# default: Load the model on the available device(s)
model = Qwen3_5MoeForConditionalGeneration.from_pretrained(
    model_id, dtype="auto", device_map="auto"#, use_cache = False
)


processor = AutoProcessor.from_pretrained(model_id)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
            },
            {"type": "text", "text": "Describe the image."},
        ],
    }
]

# Preparation for inference
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)

print(inputs)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=256,do_sample=True,temperature=0.7,     # ← 1.0より小さいほど保守的
    top_p=0.9)          # or top_k=50 など)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text[0])

For text-only usage, omit the images= argument.

llama.cpp / GGUF

For 48 GB GPU + CPU offload configurations, GGUF quantized versions (e.g., Q4_K_M) are recommended. Example offload configuration:

./llama-cli -m qwen3.5-reap-97b-a10b.Q4_K_M.gguf \
  --n-gpu-layers 38 \   # tune based on your VRAM
  --ctx-size 8192

9. Evaluation (Status)

  • This release focuses on making the REAP-pruned model available for the community.

  • Quantitative benchmarks (e.g., MMLU, GSM8K, MMBench) are still work in progress.

  • Early qualitative checks show:

    • VL behavior is preserved after pruning at this sparsity level.
    • Latency and memory usage are meaningfully reduced compared to Qwen3.5-122B-A10B, enabling 48 GB + CPU-offload deployments.
    • The conservative 22% pruning ratio appears to cause less degradation than more aggressive pruning schedules.

Community contributions with detailed benchmarks are very welcome.

10. Training & Distillation Details (High-Level)

  • Base model: Qwen/Qwen3.5-122B-A10B

  • Pruning method: REAP (Router-weighted Expert Activation Pruning)

  • Experts: 256 → 200 (22% pruned)

  • Calibration data: OpenMOSE/reap-calib-mix (mostly generated by Qwen3-235B-Instruct)

  • Post-processing:

    • Router / gating structure retained
    • Experts pruned according to REAP scoring
    • No additional large-scale pretraining in this release

Future versions may include post-pruning fine-tuning or knowledge distillation from the full 122B model to recover further performance.

11. Community & Contribution

Let's grow this model together as a community.

You are encouraged to:

  • Run benchmarks and publish results

  • Contribute scripts for:

    • Further pruning experiments
    • Quantization (GGUF, AWQ, GPTQ)
    • Long-context or domain-specific fine-tuning
    • CPU/GPU offload configuration guides for various hardware setups

  • Report issues or findings about failure modes, biases, or surprising behaviors

12. License

  • Model & code (this repository): Apache License 2.0
  • The original Qwen3.5-122B-A10B model and any downstream use must also respect their respective licenses and usage terms.

13. Acknowledgements

This architecture research and implementation was made possible with computing power and technical support from Recursal AI. We sincerely thank them for enabling this work.

https://featherless.ai/

  • Qwen team for building the Qwen3.5 family of models.
  • Cerebras Research for the REAP method and reference implementation: https://github.com/CerebrasResearch/reap
  • OpenMOSE community for experimentation, engineering, and calibration data generation.

2025 OpenMOSE

Author: OpenMOSE

Likes: 3

Downloads: 0

Tags: safetensors, qwen3_5_moe, base_model:Qwen/Qwen3.5-122B-A10B, base_model:finetune:Qwen/Qwen3.5-122B-A10B, license:apache-2.0, region:us

zerdovzad/Nord-AI

license: apache-2.0 language:

  • en pipeline_tag: text-generation tags:
  • snn

language:

  • en tags:
  • spiking-neural-network
  • SNN
  • neuromorphic
  • language-model
  • from-scratch
  • energy-efficient

⚡ Nord — Spiking Neural Network Language Model (144M)

The first pure SNN language model with a fully original architecture, trained from scratch.

Model Description

Nord is a 144M-parameter Spiking Neural Network (SNN) for text generation. It uses biologically-inspired neurons with membrane potentials, firing thresholds, and binary spikes. Unlike other SNN language models, Nord was trained entirely from scratch — no transformer teacher, no distillation, no ANN-to-SNN conversion.

Key Features

| Feature | Details | |---------|---------| | Parameters | 144.3M | | Architecture | Original (not RWKV, not Transformer) | | Training method | From scratch with surrogate gradients | | Training data | FineWeb-Edu | | Sparsity (training) | 97% | | Sparsity (inference) | 97-99.8% | | Online learning | STDP active during inference | | Mobile deployment | Android via Termux | | Training cost | ~$10 USD |

Architecture

Nord combines five mechanisms from different subfields:

  • LeakyClamp — Prevents gradient death in deep SNN layers
  • Multi-Scale Temporal Encoding — T_fast=8 + T_slow=2 timesteps
  • Associative Cascade — Chain reactions keep sparse networks alive
  • Temporal Co-firing Resonance — Feature binding without attention
  • Reward-Modulated STDP — Aligns Hebbian learning with backprop

Model Configuration

d_model: 512
n_layers: 6
n_heads: 8
d_ff: 1024
T_fast: 8
T_slow: 2
max_seq_len: 512
vocab_size: 128,256
tokenizer: Llama-3.2 (meta-llama/Llama-3.2-1B)

Training

  • Dataset: FineWeb-Edu (~950M tokens, 10GB subset)
  • Hardware: RTX A5000 24GB (rented on Vast.ai at $0.177/hr)
  • Optimizer: AdamW (lr=6e-4, weight_decay=0.05)
  • Batch size: 48 (4 × grad_accum=12)
  • Sequence length: 512
  • Loss progression: 6.28 (1k) → 5.30 (5k) → 5.00 (10k) → 4.59 (34k)

Usage

import torch
from nord_core import NordConfig, NordModel
from transformers import AutoTokenizer

# Load
ckpt = torch.load("nord_latest.pt", map_location="cuda")
cfg = NordConfig(**ckpt["config"])
model = NordModel(cfg).cuda()
model.load_state_dict(ckpt["model_state_dict"])
model.eval()

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")

Or use the interactive chat:

python chat.py

Generation Examples (Step 34k, loss 4.59)

Prompt: "If you don't write properly I will delete your file"

Nord: "(I'm really careful) that a project is pretty good for the following reasons... The file will then turn this data into your code. It's not an algorithm to say that all the file is a project called 'read'. In order to get a lot of information on you are using this program, and we'll have no more knowledge about them!"

Prompt: "bro my laptop have problems and off wifi how on wifi"

Nord: "The project has been carried out by researchers at a site in 2013, including three main projects that work with wireless phones... using Wi-Fi sensors to monitor their devices such as wireless LANs... network that would have been created using more than 2% of 4 million people to use Bluetooth technology."

Spike Statistics

| Context | Sparsity | Interpretation | |---------|----------|----------------| | Familiar topic | 99.8% | Confident — minimal neural activity | | Training | 97% | Active learning — neurons spiking | | Out-of-distribution | 77% | Uncertain — massive activation |

Sparsity functions as a built-in uncertainty detector — no separate calibration needed.

Limitations

  • Repetition remains an issue (mitigated with repetition penalty in decoding)
  • Not competitive with GPT-2 in raw quality
  • Scaling above 144M is untested
  • No formal benchmark evaluation yet
  • Hallucination present (generates plausible but fictional details)

Comparison with Other SNN Language Models

| Model | Params | From Scratch? | Architecture | |-------|--------|:-------------:|-------------| | Nord | 144M | ✅ | Fully original | | SpikeGPT | 216M | ✅ | Modified RWKV | | SpikeLLM | 7-70B | ❌ | Converted LLaMA | | SpikeBERT | ~110M | ❌ | Distilled from BERT | | BrainTransformers | 3B | ❌ | Converted Qwen2 |

Citation

@misc{nord2025,
  title={Nord: A Spiking Neural Network Language Model Trained from Scratch},
  author={zerdovzad},
  year={2025},
  url={https://github.com/gtausa197-svg/-Project-Nord-Spiking-Neural-Network-Language-Model}
}

About

Built by an 18-year-old electronics student from Ukraine, studying in Norway. No PhD, no team, no funding.

Author: zerdovzad

Likes: 3

Downloads: 0

Tags: snn, text-generation, en, license:apache-2.0, region:us

Salesforce/GPA-GUI-Detector

license: mit library_name: ultralytics tags:

  • object-detection
  • yolo
  • gui
  • ui-detection
  • omniparser pipeline_tag: object-detection

GPA-GUI-Detector

A YOLO-based GUI element detection model for detecting interactive UI elements (icons, buttons, etc.) on screen for GUI Process Automation. This model is finetuned from the OmniParser ecosystem.

Model

The model weight file is model.pt. It is a YOLO model trained with the Ultralytics framework.

Installation

pip install ultralytics

Usage

Basic Inference

from ultralytics import YOLO

model = YOLO("model.pt")
results = model("screenshot.png")

Detection with Custom Parameters

from ultralytics import YOLO
from PIL import Image

# Load the model
model = YOLO("model.pt")

# Run inference with custom confidence and image size
results = model.predict(
    source="screenshot.png",
    conf=0.05,        # confidence threshold
    imgsz=640,        # input image size
    iou=0.7,          # NMS IoU threshold
)

# Parse results
boxes = results[0].boxes.xyxy.cpu().numpy()   # bounding boxes in [x1, y1, x2, y2]
scores = results[0].boxes.conf.cpu().numpy()  # confidence scores

# Draw results on image
img = Image.open("screenshot.png")
for box, score in zip(boxes, scores):
    x1, y1, x2, y2 = box
    print(f"Detected UI element at [{x1:.0f}, {y1:.0f}, {x2:.0f}, {y2:.0f}] (conf: {score:.2f})")

# Or save the annotated image directly
results[0].save("result.png")

Integration with OmniParser

import sys
sys.path.append("/path/to/OmniParser")

from util.utils import get_yolo_model, predict_yolo
from PIL import Image

model = get_yolo_model("model.pt")
image = Image.open("screenshot.png")

boxes, confidences, phrases = predict_yolo(
    model=model,
    image=image,
    box_threshold=0.05,
    imgsz=640,
    scale_img=False,
    iou_threshold=0.7,
)

for i, (box, conf) in enumerate(zip(boxes, confidences)):
    print(f"Element {i}: box={box.tolist()}, confidence={conf:.2f}")

Example

Detection results on a sample screenshot (1920x1080) from the ScreenSpot-Pro benchmark (conf=0.05, iou=0.1, imgsz=1280).

Input Screenshot

<p align="center"> <img src="images/example_input.png" width="80%" alt="Input Screenshot"/> </p> <table> <tr> <th align="center">OmniParser V2</th> <th align="center">GPA-GUI-Detector</th> </tr> <tr> <td align="center"><img src="images/example_omniparser.png" width="92%" alt="OmniParser V2"/></td> <td align="center"><img src="images/example_gpa.png" width="99%" alt="GPA-GUI-Detector"/></td> </tr> </table>

License

This model is released under the MIT License.

Author: Salesforce

Likes: 3

Downloads: 0

Tags: ultralytics, object-detection, yolo, gui, ui-detection, omniparser, license:mit, region:us

Naphula/Meme-Trix-MoE-14B-A8B-v2

license: apache-2.0 base_model:

  • OccultAI/Morpheus-8B-v2
  • SicariusSicariiStuff/Assistant_Pepe_8B tags:
  • merge
  • llama
  • occult
  • uncensored
  • moe datasets:
  • OccultAI/Matrix_77
  • SicariusSicariiStuff/UBW_Tapestries language:
  • en library_name: transformers widget:
    • text: "Meme-Trix MoE 14B A8B v2" output: url: https://cdn-uploads.huggingface.co/production/uploads/68e840caa318194c44ec2a04/hEyahuR43IMF5TC4XrHGS.png

[!CAUTION] <span style="color:red; font-weight:bold">⚠️ Warning:</span> This model can produce narratives and RP that contain violent and graphic erotic content. Adjust your system prompt accordingly, and use Llama 3 chat template.

Meme-Trix MoE 14B A8B v2

Meme-Trix

A custom built Llama 3.1 8B MoE (Mixture of Experts) merge which combines Morpheus v2 with Assistant Pepe. This should be superior to v1 and produce more intelligent responses.

If you want to merge custom Llama MoE you can add these scripts to your mergekit environment:

Then assign the num_experts_per_tok in config.json (or the config.yaml)

Recommended Settings

(bolded kobold non-defaults)

  • Temp 1.0
  • TopNSigma 1.25
  • Min-P 0.1
  • Repetition Penalty 1.08
  • Top-P 1.0
  • Top-K 100
  • Top-A 0
  • Typical Sampling 1
  • Tail-Free Sampling 1
  • Presence Penalty 0
  • Sampler Seed -1
  • Rp.Range 360
  • Rp.Slope 0.7
  • Smoothing Factor 0
  • Smoothing Curve 1
  • DynaTemp 0
  • Mirostat Mode OFF ("2" enhances creativity but also errors)
  • Mirostat Tau 5
  • Mirostat Eta 0.1
  • DRY Multiplier 0.8
  • DRY Base 1.75
  • DRY A.Len 2
  • DRY L.Len 320
  • XTC Threshold 0.1
  • XTC Probability 0.08 (The "Anti-Cliche" Shield)
  • DynaTemp ON (The "Poor Man's Fading Mirostat")
  • Minimum Temperature 0.65
  • Maximum Temperature 1.35
  • Temperature 1.0
  • DynaTemp-Range 0.35
  • DynaTemp-Exponent 1

Author: Naphula

Likes: 3

Downloads: 0

Tags: transformers, safetensors, mixtral, text-generation, merge, llama, occult, uncensored, moe, conversational, en, dataset:OccultAI/Matrix_77, dataset:SicariusSicariiStuff/UBW_Tapestries, base_model:OccultAI/Morpheus-8B-v2, base_model:merge:OccultAI/Morpheus-8B-v2, base_model:SicariusSicariiStuff/Assistant_Pepe_8B, base_model:merge:SicariusSicariiStuff/Assistant_Pepe_8B, license:apache-2.0, text-generation-inference, endpoints_compatible, region:us

QuantTrio/Qwen3.5-397B-A17B-AWQ

library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3.5-397B-A17B/blob/main/LICENSE pipeline_tag: image-text-to-text tags:

  • vLLM
  • AWQ base_model:
    • Qwen/Qwen3.5-397B-A17B base_model_relation: quantized

Qwen3.5-397B-A17B-AWQ

Base model: Qwen/Qwen3.5-397B-A17B

This repo quantizes the model using data-free quantization (no calibration dataset required).

【Dependencies / Installation】

vllm>=0.16.0rc2.dev447
transformers>=5.3.0.dev0

As of 2026-02-25, make sure your system has cuda12.8 installed.

Then, create a fresh Python environment (e.g. python3.12 venv) and run:

pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly

# Note: important for old containers, or could face run time error like
# subprocess.CalledProcessError: 
# Command '['ninja', '-v', '-C', 
# '/root/.cache/flashinfer/0.6.3/90a/cached_ops/trtllm_comm',
# '-f', '/root/.cache/flashinfer/0.6.3/90a/cached_ops/trtllm_comm/build.ninja']'
# returned non-zero exit status 1. 
rm -rf ~/.cache/flashinfer

# upgrade transformers so that applications could properly execute tool calls
pip install -U "transformers @ git+https://github.com/huggingface/transformers.git@f2ba019"
# locate modeling_rope_utils.py line 651 to fix a simple bug
TF_FILE="$(python -m pip show transformers | awk -F': ' '/^Location:/{print $2}')/transformers/modeling_rope_utils.py" && echo "$TF_FILE"
NEW_LINE='            ignore_keys_at_rope_validation = set(ignore_keys_at_rope_validation) | {"partial_rotary_factor"}' \
perl -i.bak -pe 'if ($. == 651) { $_ = $ENV{NEW_LINE} . "\n" }' "$TF_FILE"

vLLM Official Guide

【vLLM Startup Command】

<i>Note: When launching with TP=8, include --enable-expert-parallel; otherwise the expert tensors wouldn’t be evenly sharded across GPU devices.</i>

export VLLM_USE_DEEP_GEMM=0
export VLLM_USE_FLASHINFER_MOE_FP16=1
export VLLM_USE_FLASHINFER_SAMPLER=0
export OMP_NUM_THREADS=4

vllm serve \
    __YOUR_PATH__/QuantTrio/Qwen3.5-397B-A17B-AWQ \
    --served-model-name MY_MODEL \
    --swap-space 16 \
    --max-num-seqs 32 \
    --max-model-len 32768  \
    --gpu-memory-utilization 0.9 \
    --tensor-parallel-size 4 \
    --enable-expert-parallel \ 
    --enable-auto-tool-choice \    
    --tool-call-parser qwen3_coder \
    --reasoning-parser qwen3 \
    --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}' \
    --trust-remote-code \
    --host 0.0.0.0 \
    --port 8000

【Logs】

2026-02-25
1. Initial commit

【Model Files】

| File Size | Last Updated | |-----------|--------------| | 228GiB | 2026-02-25 |

【Model Download】

from huggingface_hub import snapshot_download
snapshot_download('QuantTrio/Qwen3.5-397B-A17B-AWQ', cache_dir="your_local_path")

【Overview】

Qwen3.5-397B-A17B

<img width="400px" src="https://qianwen-res.oss-accelerate.aliyuncs.com/logo_qwen3.5.png">

Qwen Chat

[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.

These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc.

[!Tip] For users seeking managed, scalable inference without infrastructure maintenance, the official Qwen API service is provided by Alibaba Cloud Model Studio.

In particular, Qwen3.5-Plus is the hosted version corresponding to Qwen3.5-397B-A17B with more production features, e.g., 1M context length by default, official built-in tools, and adaptive tool use. For more information, please refer to the User Guide.

Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.

Qwen3.5 Highlights

Qwen3.5 features the following enhancement:

  • Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.

  • Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.

  • Scalable RL Generalization: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.

  • Global Linguistic Coverage: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.

  • Next-Generation Training Infrastructure: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration.

Benchmark Results

For more details, please refer to our blog post Qwen3.5.

Model Overview

  • Type: Causal Language Model with Vision Encoder
  • Training Stage: Pre-training & Post-training
  • Language Model
    • Number of Parameters: 397B in total and 17B activated
    • Hidden Dimension: 4096
    • Token Embedding: 248320 (Padded)
    • Number of Layers: 60
      • Hidden Layout: 15 * (3 * (Gated DeltaNet -> MoE) -> 1 * (Gated Attention -> MoE))
    • Gated DeltaNet:
      • Number of Linear Attention Heads: 64 for V and 16 for QK
      • Head Dimension: 128
    • Gated Attention:
      • Number of Attention Heads: 32 for Q and 2 for KV
      • Head Dimension: 256
      • Rotary Position Embedding Dimension: 64
    • Mixture Of Experts
      • Number of Experts: 512
      • Number of Activated Experts: 10 Routed + 1 Shared
      • Expert Intermediate Dimension: 1024
    • LM Output: 248320 (Padded)
    • MTP: trained with multi-steps
  • Context Length: 262,144 natively and extensible up to 1,010,000 tokens.

Benchmark Results

Language

<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;max-width:900px;margin:0 auto;padding:16px 0"> <table style="width:100%;border-collapse:collapse;font-size:13px"> <thead><tr> <th style="padding:10px 12px;text-align:left;font-weight:600;border-bottom:2px solid #7c3aed;color:#7c3aed"></th> <th style="padding:10px 12px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">GPT5.2</th> <th style="padding:10px 12px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Claude 4.5 Opus</th> <th style="padding:10px 12px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Gemini-3 Pro</th> <th style="padding:10px 12px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3-Max-Thinking</th> <th style="padding:10px 12px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">K2.5-1T-A32B</th> <th style="padding:10px 12px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3.5-397B-A17B</th> </tr></thead> <tbody> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Knowledge</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMLU-Pro</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.8</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMLU-Redux</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">95.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">95.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">95.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">94.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">94.9</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SuperGPQA</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.4</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">C-Eval</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">94.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.0</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Instruction Following</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">IFEval</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">94.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.6</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">IFBench</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">58.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.5</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MultiChallenge</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">57.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">54.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">64.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.6</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Long Context</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">AA-LCR</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">72.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.7</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">LongBench v2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">54.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">64.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.2</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">STEM</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">GPQA</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.4</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">HLE</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">35.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">30.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">37.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">30.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">30.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">28.7</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">HLE-Verified¹</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">43.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">38.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">48</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">37.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">37.6</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Reasoning</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">LiveCodeBench v6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.6</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">HMMT Feb 25</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">99.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">97.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">98.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">95.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">94.8</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">HMMT Nov 25</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">100</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">94.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.7</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">IMOAnswerBench</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.9</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">AIME26</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">96.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.3</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">General Agent</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">BFCL-V4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">72.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">72.9</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">TAU2-Bench</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.7</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">VITA-Bench</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">38.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">56.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">51.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">40.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">49.7</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">DeepPlanning</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">44.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">33.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">23.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">28.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">14.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">34.3</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Tool Decathlon</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">43.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">43.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">36.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">18.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">27.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">38.3</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MCP-Mark</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">57.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">42.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">53.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">33.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">29.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">46.1</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Search Agent³</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">HLE w/ tool</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">45.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">43.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">45.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">49.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">50.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">48.3</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">BrowseComp</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">59.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">53.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--/74.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.0/78.6</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">BrowseComp-zh</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">66.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.3</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">WideSearch</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">57.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">72.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.0</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Seal-0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">45.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">47.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">45.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">46.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">57.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">46.9</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Multilingualism</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMMLU</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.5</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMLU-ProX</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.7</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">NOVA-63</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">54.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">56.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">56.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">54.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">56.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">59.1</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">INCLUDE</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.6</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Global PIQA</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.8</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">PolyMATH</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">64.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">43.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.3</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">WMT24++</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.9</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MAXIFE</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">72.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.2</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Coding Agent</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.4</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Multilingual</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">72.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">66.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.3</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SecCodeBench</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">57.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.3</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Terminal Bench 2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">54.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">59.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">54.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">22.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">50.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">52.5</td> </tr> </tbody> </table> <p style="margin-top:12px;font-size:11px;opacity:0.7"> * HLE-Verified: a verified and revised version of Humanity’s Last Exam (HLE), accompanied by a transparent, component-wise verification protocol and a fine-grained error taxonomy. We open-source the dataset at https://huggingface.co/datasets/skylenage/HLE-Verified.<br> * TAU2-Bench: we follow the official setup except for the airline domain, where all models are evaluated by applying the fixes proposed in the Claude Opus 4.5 system card.<br> * MCPMark: GitHub MCP server uses v0.30.3 from api.githubcopilot.com; Playwright tool responses are truncated at 32k tokens.<br> * Search Agent: most search agents built on our model adopt a simple context-folding strategy(256k): once the cumulative Tool Response length reaches a preset threshold, earlier Tool Responses are pruned from the history to keep the context within limits.<br> * BrowseComp: we tested two strategies, simple context-folding achieved a score of 69.0, while using the same discard-all strategy as DeepSeek-V3.2 and Kimi K2.5 achieved 78.6.<br> * WideSearch: we use a 256k context window without any context management.<br> * MMLU-ProX: we report the averaged accuracy on 29 languages.<br> * WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL.<br> * MAXIFE: we report the accuracy on English + multilingual original prompts (totally 23 settings).<br> * Empty cells (--) indicate scores not yet available or not applicable.<br> </p> </div>

Vision Language

<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;max-width:900px;margin:0 auto;padding:16px 0"> <table style="width:100%;border-collapse:collapse;font-size:13px"> <thead><tr> <th style="padding:10px 12px;text-align:left;font-weight:600;border-bottom:2px solid #7c3aed;color:#7c3aed"></th> <th style="padding:10px 12px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">GPT5.2</th> <th style="padding:10px 12px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Claude 4.5 Opus</th> <th style="padding:10px 12px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Gemini-3 Pro</th> <th style="padding:10px 12px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3-VL-235B-A22B</th> <th style="padding:10px 12px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">K2.5-1T-A32B</th> <th style="padding:10px 12px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3.5-397B-A17B</th> </tr></thead> <tbody> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">STEM and Puzzle</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMMU</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.0</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMMU-Pro</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.0</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MathVision</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.6</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Mathvista(mini)</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.3</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">We-Math</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.9</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">DynaMath</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.3</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">ZEROBench</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">10</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">12</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">ZEROBench_sub</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">33.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">28.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">39.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">28.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">33.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.0</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">BabyVision</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">34.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">14.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">49.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">22.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">36.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">52.3/43.3</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">General VQA</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">RealWorldQA</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.9</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMStar</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.8</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">HallusionBench</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">64.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">66.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">71.4</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMBench<sub><small>EN-DEV-v1.1</small></sub></td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">94.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.7</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SimpleVQA</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">55.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">71.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.1</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Text Recognition and Document Understanding</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">OmniDocBench1.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.8</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">CharXiv(RQ)</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">66.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.8</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMLongBench-Doc</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">56.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">58.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.5</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">CC-OCR</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.0</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">AI2D_TEST</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">94.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.9</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">OCRBench</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.1</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Spatial Intelligence</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">ERQA</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">59.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">46.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">52.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.5</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">CountBench</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">97.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">94.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">97.2</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">RefCOCO(avg)</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">92.3</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">ODInW13</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">46.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">43.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">47.0</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">EmbSpatialBench</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.5</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">RefSpatialBench</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.6</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">LingoQA</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">72.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">66.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.6</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">V*</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">95.8/91.1</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Hypersim</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">11.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">12.5</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SUNRGBD</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">34.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">38.3</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Nuscene</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">13.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">16.0</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Video Understanding</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">VideoMME<sub><small>(w sub.)</sub></small></td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.5</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">VideoMME<sub><small>(w/o sub.)</sub></small></td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.7</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">VideoMMMU</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.7</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MLVU (M-Avg)</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">83.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.7</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MVBench</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.6</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">LVBench</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">57.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.5</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMVU</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.8</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.5</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">71.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.4</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Visual Agent</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">ScreenSpot Pro</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">45.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">72.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.6</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">OSWorld-Verified</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">38.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">66.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">38.1</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.2</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">AndroidWorld</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">--</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">66.8</td> </tr> <tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Medical VQA</td></tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SLAKE</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.4</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">54.7</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.9</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">PMC-VQA</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">58.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">59.9</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.2</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">64.2</td> </tr> <tr> <td style="padding:7px 12px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MedXpertQA-MM</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.0</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">47.6</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.3</td> <td style="padding:7px 12px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.0</td> </tr> </tbody> </table> <p style="margin-top:12px;font-size:11px;opacity:0.7"> * MathVision：our model’s score is evaluated using a fixed prompt, e.g., “Please reason step by step, and put your final answer within \boxed{}.” For other models, we report the higher score between runs with and without the \boxed{} formatting.<br> * BabyVision: our model’s score is reported with CI (Code Interpreter) enabled; without CI, the result is 43.3.<br> * V*: our model’s score is reported with CI (Code Interpreter) enabled; without CI, the result is 91.1.<br> * Empty cells (--) indicate scores not yet available or not applicable.<br> </p> </div>

Quickstart

[!Important] Qwen3.5 models operate in thinking mode by default, generating thinking content signified by <think>\n...</think>\n\n before producing the final responses. To disable thinking content and obtain direct response, refer to the examples here.

For streamlined integration, we recommend using Qwen3.5 via APIs. Below is a guide to use Qwen3.5 via OpenAI-compatible API.

Serving Qwen3.5

Qwen3.5 can be served via APIs with popular inference frameworks. In the following, we show example commands to launch OpenAI-Compatible API servers for Qwen3.5 models.

[!Important] Inference efficiency and throughput vary significantly across frameworks. We recommend using the latest framework versions to ensure optimal performance and compatibility. For production workloads or high-throughput scenarios, dedicated serving engines such as SGLang or vLLM are strongly recommended.

[!Important] The model has a default context length of 262,144 tokens. If you encounter out-of-memory (OOM) errors, consider reducing the context window. However, because Qwen3.5 leverages extended context for complex tasks, we advise maintaining a context length of at least 128K tokens to preserve thinking capabilities.

SGLang

SGLang is a fast serving framework for large language models and vision language models. SGLang from the main branch of the open-source repository is required for Qwen3.5, which can be installed using the following command in a fresh environment:

uv pip install 'git+https://github.com/sgl-project/sglang.git#subdirectory=python&egg=sglang[all]'

See its documentation for more details.

The following will create API endpoints at http://localhost:8000/v1:

  • Standard Version: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs.

    python -m sglang.launch_server --model-path Qwen/Qwen3.5-397B-A17B --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3

  • Tool Use: To support tool use, you can use the following command.

    python -m sglang.launch_server --model-path Qwen/Qwen3.5-397B-A17B --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --tool-call-parser qwen3_coder

  • Multi-Token Prediction (MTP): The following command is recommended for MTP:

    python -m sglang.launch_server --model-path Qwen/Qwen3.5-397B-A17B --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --speculative-algo NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4

vLLM

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. vLLM from the main branch of the open-source repository is required for Qwen3.5, which can be installed using the following command in a fresh environment:

uv pip install vllm --torch-backend=auto --extra-index-url https://wheels.vllm.ai/nightly

See its documentation for more details.

For detailed Qwen3.5 usage guide, see the vLLM Qwen3.5 recipe.

The following will create API endpoints at http://localhost:8000/v1:

  • Standard Version: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs.

    vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3

  • Tool Call: To support tool use, you can use the following command.

    vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder

  • Multi-Token Prediction (MTP): The following command is recommended for MTP:

    vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'

  • Text-Only: The following command skips the vision encoder and multimodal profiling to free up memory for additional KV cache:

    vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --language-model-only

Hugging Face Transformers

Hugging Face Transformers contains a lightweight server which can be used for quick testing and moderate load deployment. The latest transformers is required for Qwen3.5:

pip install "transformers[serving] @ git+https://github.com/huggingface/transformers.git@main"

See its documentation for more details. Please also make sure torchvision and pillow are installed.

Then, run transformers serve to launch a server with API endpoints at http://localhost:8000/v1; it will place the model on accelerators if available:

transformers serve --force-model Qwen/Qwen3.5-397B-A17B --port 8000 --continuous-batching

Using Qwen3.5 via the Chat Completions API

The chat completions API is accessible via standard HTTP requests or OpenAI SDKs. Here, we show examples using the OpenAI Python SDK.

Before starting, make sure it is installed and the API key and the API base URL is configured, e.g.:

pip install -U openai

# Set the following accordingly
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"

[!Tip] We recommend using the following set of sampling parameters for generation

  • Thinking mode: temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
  • Instruct (or non-thinking) mode: temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0

Please note that the support for sampling parameters varies according to inference frameworks.

Text-Only Input

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {"role": "user", "content": "Type \"I love Qwen3.5\" backwards"},
]

chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.5-397B-A17B",
    messages=messages,
    max_tokens=81920,
    temperature=0.6,
    top_p=0.95,
    extra_body={
        "top_k": 20,
    }, 
)
print("Chat response:", chat_response)

Image Input

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/CI_Demo/mathv-1327.jpg"
                }
            },
            {
                "type": "text",
                "text": "The centres of the four illustrated circles are in the corners of the square. The two big circles touch each other and also the two little circles. With which factor do you have to multiply the radii of the little circles to obtain the radius of the big circles?\nChoices:\n(A) $\\frac{2}{9}$\n(B) $\\sqrt{5}$\n(C) $0.8 \\cdot \\pi$\n(D) 2.5\n(E) $1+\\sqrt{2}$"
            }
        ]
    }
]

response = client.chat.completions.create(
    model="Qwen/Qwen3.5-397B-A17B",
    messages=messages,
    max_tokens=81920,
    temperature=0.6,
    top_p=0.95,
    extra_body={
        "top_k": 20,
    }, 
)
print("Chat response:", chat_response)

Video Input

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "video_url",
                "video_url": {
                    "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/video/N1cdUjctpG8.mp4"
                }
            },
            {
                "type": "text",
                "text": "How many porcelain jars were discovered in the niches located in the primary chamber of the tomb?"
            }
        ]
    }
]

# When vLLM is launched with `--media-io-kwargs '{"video": {"num_frames": -1}}'`,
# video frame sampling can be configured via `extra_body` (e.g., by setting `fps`).
# This feature is currently supported only in vLLM.
#
# By default, `fps=2` and `do_sample_frames=True`.
# With `do_sample_frames=True`, you can customize the `fps` value to set your desired video sampling rate.
response = client.chat.completions.create(
    model="Qwen/Qwen3.5-397B-A17B",
    messages=messages,
    max_tokens=81920,
    temperature=0.6,
    top_p=0.95,
    extra_body={
        "top_k": 20,
        "mm_processor_kwargs": {"fps": 2, "do_sample_frames": True},
    }, 
)

print("Chat response:", chat_response)

Instruct (or Non-Thinking) Mode

[!Important] Qwen3.5 does not officially support the soft switch of Qwen3, i.e., /think and /nothink.

Qwen3.5 will think by default before response. You can obtain direct response from the model without thinking by configuring the API parameters. For example,

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/RealWorld/RealWorld-04.png"
                }
            },
            {
                "type": "text",
                "text": "Where is this?"
            }
        ]
    }
]

chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.5-397B-A17B",
    messages=messages,
    max_tokens=32768,
    temperature=0.7,
    top_p=0.8,
    presence_penalty=1.5,
    extra_body={
        "top_k": 20,
        "chat_template_kwargs": {"enable_thinking": False},
    }, 
)
print("Chat response:", chat_response)

[!Note] If you are using APIs from Alibaba Cloud Model Studio, in addition to changing model, please use "enable_thinking": False instead of "chat_template_kwargs": {"enable_thinking": False}.

Agentic Usage

Qwen3.5 excels in tool calling capabilities.

Qwen-Agent

We recommend using Qwen-Agent to quickly build Agent applications with Qwen3.5.

To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.

import os
from qwen_agent.agents import Assistant

# Define LLM
# Using Alibaba Cloud Model Studio
llm_cfg = {
    # Use the OpenAI-compatible model service provided by DashScope:
    'model': 'Qwen3.5-397B-A17B',
    'model_type': 'qwenvl_oai',
    'model_server': 'https://dashscope.aliyuncs.com/compatible-mode/v1',
    'api_key': os.getenv('DASHSCOPE_API_KEY'),

    'generate_cfg': {
        'use_raw_api': True,
        # When using Dash Scope OAI API, pass the parameter of whether to enable thinking mode in this way
        'extra_body': {
            'enable_thinking': True
        },
    },
}

# Using OpenAI-compatible API endpoint.
# functionality of the deployment frameworks and let Qwen-Agent automate the related operations.
#
# llm_cfg = {
#     # Use your own model service compatible with OpenAI API by vLLM/SGLang:
#     'model': 'Qwen/Qwen3.5-397B-A17B',
#     'model_type': 'qwenvl_oai',
#     'model_server': 'http://localhost:8000/v1',  # api_base
#     'api_key': 'EMPTY',
#
#     'generate_cfg': {
#         'use_raw_api': True,
#         # When using vLLM/SGLang OAI API, pass the parameter of whether to enable thinking mode in this way
#         'extra_body': {
#             'chat_template_kwargs': {'enable_thinking': True}
#         },
#     },
# }

# Define Tools
tools = [
    {'mcpServers': {  # You can specify the MCP configuration file
            "filesystem": {
                "command": "npx",
                "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/xxxx/Desktop"]
            }
        }
    }
]

# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)

# Streaming generation
messages = [{'role': 'user', 'content': 'Help me organize my desktop.'}]
for responses in bot.run(messages=messages):
    pass
print(responses)

# Streaming generation
messages = [{'role': 'user', 'content': 'Develop a dog website and save it on the desktop'}]
for responses in bot.run(messages=messages):
    pass
print(responses)

Qwen Code

Qwen Code is an open-source AI agent for the terminal, optimized for Qwen models. It helps you understand large codebases, automate tedious work, and ship faster.

For more information, please refer to Qwen Code.

Processing Ultra-Long Texts

Qwen3.5 natively supports context lengths of up to 262,144 tokens. For long-horizon tasks where the total length (including both input and output) exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively., e.g., YaRN.

YaRN is currently supported by several inference frameworks, e.g., transformers, vllm and sglang. In general, there are two approaches to enabling YaRN for supported frameworks:

  • Modifying the model configuration file: In the config.json file, change the rope_parameters fields in text_config to:

    {
    "mrope_interleaved": true,
    "mrope_section": [
        11,
        11,
        10
    ],
    "rope_type": "yarn",
    "rope_theta": 10000000,
    "partial_rotary_factor": 0.25,
    "factor": 4.0,
    "original_max_position_embeddings": 262144,
}

  • Passing command line arguments:

    For vllm, you can use

    VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve ... --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --max-model-len 1010000

    For sglang, you can use

    SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server ... --json-model-override-args '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --context-length 1010000

[!NOTE] All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise modifying the rope_parameters configuration only when processing long contexts is required. It is also recommended to modify the factor as needed. For example, if the typical context length for your application is 524,288 tokens, it would be better to set factor as 2.0.

Best Practices

To achieve optimal performance, we recommend the following settings:

  1. Sampling Parameters:

    • We suggest using Temperature=0.6, TopP=0.95, TopK=20, and MinP=0 for thinking mode and using Temperature=0.7, TopP=0.8, TopK=20, and MinP=0 for non-thinking mode.
    • For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.

  2. Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 81,920 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.

  3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.

    • Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
    • Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g., "answer": "C"."

  4. No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.

  5. Long Video Understanding: To optimize inference efficiency for plain text and images, the size parameter in the released video_preprocessor_config.json is conservatively configured. It is recommended to set the longest_edge parameter in the video_preprocessor_config file to 469,762,048 (corresponding to 224k video tokens) to enable higher frame-rate sampling for hour-scale videos and thereby achieve superior performance. For example,

    {"longest_edge": 469762048, "shortest_edge": 4096}

    Alternatively, override the default values via engine startup parameters. For implementation details, refer to: vLLM / SGLang.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen3.5
    title  = {{Qwen3.5}: Towards Native Multimodal Agents},
    author = {{Qwen Team}},
    month  = {February},
    year   = {2026},
    url    = {https://qwen.ai/blog?id=qwen3.5}
}

Author: QuantTrio

Likes: 3

Downloads: 0

Tags: transformers, safetensors, qwen3_5_moe, image-text-to-text, vLLM, AWQ, conversational, base_model:Qwen/Qwen3.5-397B-A17B, base_model:quantized:Qwen/Qwen3.5-397B-A17B, license:apache-2.0, endpoints_compatible, 4-bit, awq, region:us