+
+[](https://chat.qwen.ai)
+
+> [!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](https://modelstudio.alibabacloud.com/).
+>
+> 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](https://www.alibabacloud.com/help/en/model-studio/text-generation).
+
+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.
+
+
+
+
+For more details, please refer to our blog post [Qwen3.5](https://qwen.ai/blog?id=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
+
+| + | 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 | +76.4 | +
| 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.
+
| + | 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.
+