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The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency.
ollama run qwen3.5:cloud
Applications
ollama launch claude --model qwen3.5:cloud
ollama launch codex --model qwen3.5:cloud
ollama launch opencode --model qwen3.5:cloud
ollama launch openclaw --model qwen3.5:cloud
Models
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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
Language
| GPT5.2 | Claude 4.5 Opus | Gemini-3 Pro | Qwen3-Max-Thinking | K2.5-1T-A32B | Qwen3.5-397B-A17B | |
|---|---|---|---|---|---|---|
| Knowledge | ||||||
| MMLU-Pro | 87.4 | 89.5 | 89.8 | 85.7 | 87.1 | 87.8 |
| MMLU-Redux | 95.0 | 95.6 | 95.9 | 92.8 | 94.5 | 94.9 |
| SuperGPQA | 67.9 | 70.6 | 74.0 | 67.3 | 69.2 | 70.4 |
| C-Eval | 90.5 | 92.2 | 93.4 | 93.7 | 94.0 | 93.0 |
| Instruction Following | ||||||
| IFEval | 94.8 | 90.9 | 93.5 | 93.4 | 93.9 | 92.6 |
| IFBench | 75.4 | 58.0 | 70.4 | 70.9 | 70.2 | 76.5 |
| MultiChallenge | 57.9 | 54.2 | 64.2 | 63.3 | 62.7 | 67.6 |
| Long Context | ||||||
| AA-LCR | 72.7 | 74.0 | 70.7 | 68.7 | 70.0 | 68.7 |
| LongBench v2 | 54.5 | 64.4 | 68.2 | 60.6 | 61.0 | 63.2 |
| STEM | ||||||
| GPQA | 92.4 | 87.0 | 91.9 | 87.4 | 87.6 | 88.4 |
| HLE | 35.5 | 30.8 | 37.5 | 30.2 | 30.1 | 28.7 |
| HLE-Verified¹ | 43.3 | 38.8 | 48 | 37.6 | -- | 37.6 |
| Reasoning | ||||||
| LiveCodeBench v6 | 87.7 | 84.8 | 90.7 | 85.9 | 85.0 | 83.6 |
| HMMT Feb 25 | 99.4 | 92.9 | 97.3 | 98.0 | 95.4 | 94.8 |
| HMMT Nov 25 | 100 | 93.3 | 93.3 | 94.7 | 91.1 | 92.7 |
| IMOAnswerBench | 86.3 | 84.0 | 83.3 | 83.9 | 81.8 | 80.9 |
| AIME26 | 96.7 | 93.3 | 90.6 | 93.3 | 93.3 | 91.3 |
| General Agent | ||||||
| BFCL-V4 | 63.1 | 77.5 | 72.5 | 67.7 | 68.3 | 72.9 |
| TAU2-Bench | 87.1 | 91.6 | 85.4 | 84.6 | 77.0 | 86.7 |
| VITA-Bench | 38.2 | 56.3 | 51.6 | 40.9 | 41.9 | 49.7 |
| DeepPlanning | 44.6 | 33.9 | 23.3 | 28.7 | 14.5 | 34.3 |
| Tool Decathlon | 43.8 | 43.5 | 36.4 | 18.8 | 27.8 | 38.3 |
| MCP-Mark | 57.5 | 42.3 | 53.9 | 33.5 | 29.5 | 46.1 |
| Search Agent³ | ||||||
| HLE w/ tool | 45.5 | 43.4 | 45.8 | 49.8 | 50.2 | 48.3 |
| BrowseComp | 65.8 | 67.8 | 59.2 | 53.9 | --/74.9 | 69.0/78.6 |
| BrowseComp-zh | 76.1 | 62.4 | 66.8 | 60.9 | -- | 70.3 |
| WideSearch | 76.8 | 76.4 | 68.0 | 57.9 | 72.7 | 74.0 |
| Seal-0 | 45.0 | 47.7 | 45.5 | 46.9 | 57.4 | 46.9 |
| Multilingualism | ||||||
| MMMLU | 89.5 | 90.1 | 90.6 | 84.4 | 86.0 | 88.5 |
| MMLU-ProX | 83.7 | 85.7 | 87.7 | 78.5 | 82.3 | 84.7 |
| NOVA-63 | 54.6 | 56.7 | 56.7 | 54.2 | 56.0 | 59.1 |
| INCLUDE | 87.5 | 86.2 | 90.5 | 82.3 | 83.3 | 85.6 |
| Global PIQA | 90.9 | 91.6 | 93.2 | 86.0 | 89.3 | 89.8 |
| PolyMATH | 62.5 | 79.0 | 81.6 | 64.7 | 43.1 | 73.3 |
| WMT24++ | 78.8 | 79.7 | 80.7 | 77.6 | 77.6 | 78.9 |
| MAXIFE | 88.4 | 79.2 | 87.5 | 84.0 | 72.8 | 88.2 |
| Coding Agent | ||||||
| SWE-bench Verified | 80.0 | 80.9 | 76.2 | 75.3 | 76.8 | 76.2 |
| SWE-bench Multilingual | 72.0 | 77.5 | 65.0 | 66.7 | 73.0 | 69.3 |
| SecCodeBench | 68.7 | 68.6 | 62.4 | 57.5 | 61.3 | 68.3 |
| Terminal Bench 2 | 54.0 | 59.3 | 54.2 | 22.5 | 50.8 | 52.5 |
* 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.
* 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.
* MCPMark: GitHub MCP server uses v0.30.3 from api.githubcopilot.com; Playwright tool responses are truncated at 32k tokens.
* 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.
* 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.
* WideSearch: we use a 256k context window without any context management.
* MMLU-ProX: we report the averaged accuracy on 29 languages.
* WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL.
* MAXIFE: we report the accuracy on English + multilingual original prompts (totally 23 settings).
* Empty cells (--) indicate scores not yet available or not applicable.
Vision Language
| GPT5.2 | Claude 4.5 Opus | Gemini-3 Pro | Qwen3-VL-235B-A22B | K2.5-1T-A32B | Qwen3.5-397B-A17B | |
|---|---|---|---|---|---|---|
| STEM and Puzzle | ||||||
| MMMU | 86.7 | 80.7 | 87.2 | 80.6 | 84.3 | 85.0 |
| MMMU-Pro | 79.5 | 70.6 | 81.0 | 69.3 | 78.5 | 79.0 |
| MathVision | 83.0 | 74.3 | 86.6 | 74.6 | 84.2 | 88.6 |
| Mathvista(mini) | 83.1 | 80.0 | 87.9 | 85.8 | 90.1 | 90.3 |
| We-Math | 79.0 | 70.0 | 86.9 | 74.8 | 84.7 | 87.9 |
| DynaMath | 86.8 | 79.7 | 85.1 | 82.8 | 84.4 | 86.3 |
| ZEROBench | 9 | 3 | 10 | 4 | 9 | 12 |
| ZEROBench_sub | 33.2 | 28.4 | 39.0 | 28.4 | 33.5 | 41.0 |
| BabyVision | 34.4 | 14.2 | 49.7 | 22.2 | 36.5 | 52.3⁄43.3 |
| General VQA | ||||||
| RealWorldQA | 83.3 | 77.0 | 83.3 | 81.3 | 81.0 | 83.9 |
| MMStar | 77.1 | 73.2 | 83.1 | 78.7 | 80.5 | 83.8 |
| HallusionBench | 65.2 | 64.1 | 68.6 | 66.7 | 69.8 | 71.4 |
| MMBenchEN-DEV-v1.1 | 88.2 | 89.2 | 93.7 | 89.7 | 94.2 | 93.7 |
| SimpleVQA | 55.8 | 65.7 | 73.2 | 61.3 | 71.2 | 67.1 |
| Text Recognition and Document Understanding | ||||||
| OmniDocBench1.5 | 85.7 | 87.7 | 88.5 | 84.5 | 88.8 | 90.8 |
| CharXiv(RQ) | 82.1 | 68.5 | 81.4 | 66.1 | 77.5 | 80.8 |
| MMLongBench-Doc | – | 61.9 | 60.5 | 56.2 | 58.5 | 61.5 |
| CC-OCR | 70.3 | 76.9 | 79.0 | 81.5 | 79.7 | 82.0 |
| AI2D_TEST | 92.2 | 87.7 | 94.1 | 89.2 | 90.8 | 93.9 |
| OCRBench | 80.7 | 85.8 | 90.4 | 87.5 | 92.3 | 93.1 |
| Spatial Intelligence | ||||||
| ERQA | 59.8 | 46.8 | 70.5 | 52.5 | – | 67.5 |
| CountBench | 91.9 | 90.6 | 97.3 | 93.7 | 94.1 | 97.2 |
| RefCOCO(avg) | – | – | 84.1 | 91.1 | 87.8 | 92.3 |
| ODInW13 | – | – | 46.3 | 43.2 | – | 47.0 |
| EmbSpatialBench | 81.3 | 75.7 | 61.2 | 84.3 | 77.4 | 84.5 |
| RefSpatialBench | – | – | 65.5 | 69.9 | – | 73.6 |
| LingoQA | 68.8 | 78.8 | 72.8 | 66.8 | 68.2 | 81.6 |
| V* | 75.9 | 67.0 | 88.0 | 85.9 | 77.0 | 95.8⁄91.1 |
| Hypersim | – | – | – | 11.0 | – | 12.5 |
| SUNRGBD | – | – | – | 34.9 | – | 38.3 |
| Nuscene | – | – | – | 13.9 | – | 16.0 |
| Video Understanding | ||||||
| VideoMME(w sub.) | 86 | 77.6 | 88.4 | 83.8 | 87.4 | 87.5 |
| VideoMME(w/o sub.) | 85.8 | 81.4 | 87.7 | 79.0 | 83.2 | 83.7 |
| VideoMMMU | 85.9 | 84.4 | 87.6 | 80.0 | 86.6 | 84.7 |
| MLVU (M-Avg) | 85.6 | 81.7 | 83.0 | 83.8 | 85.0 | 86.7 |
| MVBench | 78.1 | 67.2 | 74.1 | 75.2 | 73.5 | 77.6 |
| LVBench | 73.7 | 57.3 | 76.2 | 63.6 | 75.9 | 75.5 |
| MMVU | 80.8 | 77.3 | 77.5 | 71.1 | 80.4 | 75.4 |
| Visual Agent | ||||||
| ScreenSpot Pro | – | 45.7 | 72.7 | 62.0 | – | 65.6 |
| OSWorld-Verified | 38.2 | 66.3 | – | 38.1 | 63.3 | 62.2 |
| AndroidWorld | – | – | – | 63.7 | – | 66.8 |
| Medical VQA | ||||||
| SLAKE | 76.9 | 76.4 | 81.3 | 54.7 | 81.6 | 79.9 |
| PMC-VQA | 58.9 | 59.9 | 62.3 | 41.2 | 63.3 | 64.2 |
| MedXpertQA-MM | 73.3 | 63.6 | 76.0 | 47.6 | 65.3 | 70.0 |
* 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.
* BabyVision: our model’s score is reported with CI (Code Interpreter) enabled; without CI, the result is 43.3.
* V*: our model’s score is reported with CI (Code Interpreter) enabled; without CI, the result is 91.1.
* Empty cells (--) indicate scores not yet available or not applicable.