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> [!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.
Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience.
## Qwen3.6 Highlights
This release delivers substantial upgrades, particularly in
- **Agentic Coding:** the model now handles frontend workflows and repository-level reasoning with greater fluency and precision.
- **Thinking Preservation:** we've introduced a new option to retain reasoning context from historical messages, streamlining iterative development and reducing overhead.
For more details, please refer to our blog post [Qwen3.6-27B](https://qwen.ai/blog?id=qwen3.6-27b).
## Model Overview
- Type: Causal Language Model with Vision Encoder
- Training Stage: Pre-training & Post-training
- Language Model
- Number of Parameters: 27B
- Hidden Dimension: 5120
- Token Embedding: 248320 (Padded)
- Number of Layers: 64
- Hidden Layout: 16 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN))
- Gated DeltaNet:
- Number of Linear Attention Heads: 48 for V and 16 for QK
- Head Dimension: 128
- Gated Attention:
- Number of Attention Heads: 24 for Q and 4 for KV
- Head Dimension: 256
- Rotary Position Embedding Dimension: 64
- Feed Forward Network:
- Intermediate Dimension: 17408
- 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
| Qwen3.5-27B | Qwen3.5-397B-A17B | Gemma4-31B | Claude 4.5 Opus | Qwen3.6-35B-A3B | Qwen3.6-27B | |
|---|---|---|---|---|---|---|
| Coding Agent | ||||||
| SWE-bench Verified | 75.0 | 76.2 | 52.0 | 80.9 | 73.4 | 77.2 |
| SWE-bench Pro | 51.2 | 50.9 | 35.7 | 57.1 | 49.5 | 53.5 |
| SWE-bench Multilingual | 69.3 | 69.3 | 51.7 | 77.5 | 67.2 | 71.3 |
| Terminal-Bench 2.0 | 41.6 | 52.5 | 42.9 | 59.3 | 51.5 | 59.3 |
| SkillsBench Avg5 | 27.2 | 30.0 | 23.6 | 45.3 | 28.7 | 48.2 |
| QwenWebBench | 1068 | 1186 | 1197 | 1536 | 1397 | 1487 |
| NL2Repo | 27.3 | 32.2 | 15.5 | 43.2 | 29.4 | 36.2 |
| Claw-Eval Avg | 64.3 | 70.7 | 48.5 | 76.6 | 68.7 | 72.4 |
| Claw-Eval Pass^3 | 46.2 | 48.1 | 25.0 | 59.6 | 50.0 | 60.6 |
| QwenClawBench | 52.2 | 51.8 | 41.7 | 52.3 | 52.6 | 53.4 |
| Knowledge | ||||||
| MMLU-Pro | 86.1 | 87.8 | 85.2 | 89.5 | 85.2 | 86.2 |
| MMLU-Redux | 93.2 | 94.9 | 93.7 | 95.6 | 93.3 | 93.5 |
| SuperGPQA | 65.6 | 70.4 | 65.7 | 70.6 | 64.7 | 66.0 |
| C-Eval | 90.5 | 93.0 | 82.6 | 92.2 | 90.0 | 91.4 |
| STEM & Reasoning | ||||||
| GPQA Diamond | 85.5 | 88.4 | 84.3 | 87.0 | 86.0 | 87.8 |
| HLE | 24.3 | 28.7 | 19.5 | 30.8 | 21.4 | 24.0 |
| LiveCodeBench v6 | 80.7 | 83.6 | 80.0 | 84.8 | 80.4 | 83.9 |
| HMMT Feb 25 | 92.0 | 94.8 | 88.7 | 92.9 | 90.7 | 93.8 |
| HMMT Nov 25 | 89.8 | 92.7 | 87.5 | 93.3 | 89.1 | 90.7 |
| HMMT Feb 26 | 84.3 | 87.9 | 77.2 | 85.3 | 83.6 | 84.3 |
| IMOAnswerBench | 79.9 | 80.9 | 74.5 | 84.0 | 78.9 | 80.8 |
| AIME26 | 92.6 | 93.3 | 89.2 | 95.1 | 92.7 | 94.1 |
* SWE-Bench Series: Internal agent scaffold (bash + file-edit tools); temp=1.0, top_p=0.95, 200K context window. We correct some problematic tasks in the public set of SWE-bench Pro and evaluate all baselines on the refined benchmark.
* Terminal-Bench 2.0: Harbor/Terminus-2 harness; 3h timeout, 32 CPU/48 GB RAM; temp=1.0, top_p=0.95, top_k=20, max_tokens=80K, 256K ctx; avg of 5 runs.
* SkillsBench: Evaluated via OpenCode on 78 tasks (self-contained subset, excluding API-dependent tasks); avg of 5 runs.
* NL2Repo: Others are evaluated via Claude Code (temp=1.0, top_p=0.95, max_turns=900).
* QwenClawBench: A real-user-distribution Claw agent benchmark; temp=0.6, 256K ctx.
* QwenWebBench: An internal front-end code generation benchmark; bilingual (EN/CN), 7 categories (Web Design, Web Apps, Games, SVG, Data Visualization, Animation, and 3D); auto-render + multimodal judge (code/visual correctness); BT/Elo rating system.
* AIME 26: We use the full AIME 2026 (I & II), where the scores may differ from Qwen 3.5 notes.
| Qwen3.5-27B | Qwen3.5-397B-A17B | Gemma4-31B | Claude 4.5 Opus | Qwen3.6-35B-A3B | Qwen3.6-27B | |
|---|---|---|---|---|---|---|
| STEM & Puzzle | ||||||
| MMMU | 82.3 | 85.0 | 80.4 | 80.7 | 81.7 | 82.9 |
| MMMU-Pro | 75.0 | 79.0 | 76.9 | 70.6 | 75.3 | 75.8 |
| MathVista mini | 87.8 | -- | 79.3 | -- | 86.4 | 87.4 |
| DynaMath | 87.7 | 86.3 | 79.5 | 79.7 | 82.8 | 85.6 |
| VlmsAreBlind | 96.9 | -- | 87.2 | -- | 96.6 | 97.0 |
| General VQA | ||||||
| RealWorldQA | 83.7 | 83.9 | 72.3 | 77.0 | 85.3 | 84.1 |
| MMStar | 81.0 | 83.8 | 77.3 | 73.2 | 80.7 | 81.4 |
| MMBenchEN-DEV-v1.1 | 92.6 | -- | 90.9 | -- | 92.8 | 92.3 |
| SimpleVQA | 56.0 | 67.1 | 52.9 | 65.7 | 58.9 | 56.1 |
| Document Understanding | ||||||
| CharXiv RQ | 79.5 | 80.8 | 67.9 | 68.5 | 78.0 | 78.4 |
| CC-OCR | 81.0 | 82.0 | 75.7 | 76.9 | 81.9 | 81.2 |
| OCRBench | 89.4 | -- | 86.1 | -- | 90.0 | 89.4 |
| Spatial Intelligence | ||||||
| ERQA | 60.5 | 67.5 | 57.5 | 46.8 | 61.8 | 62.5 |
| CountBench | 97.8 | 97.2 | 96.1 | 90.6 | 96.1 | 97.8 |
| RefCOCO avg | 90.9 | 92.3 | -- | -- | 92.0 | 92.5 |
| EmbSpatialBench | 84.5 | -- | -- | -- | 84.3 | 84.6 |
| RefSpatialBench | 67.7 | -- | 4.7 | -- | 64.3 | 70.0 |
| Video Understanding | ||||||
| VideoMME(w sub.) | 87.0 | 87.5 | -- | 77.7 | 86.6 | 87.7 |
| VideoMMMU | 82.3 | 84.7 | 81.6 | 84.4 | 83.7 | 84.4 |
| MLVU | 85.9 | 86.7 | -- | 81.7 | 86.2 | 86.6 |
| MVBench | 74.6 | 77.6 | -- | 67.2 | 74.6 | 75.5 |
| Visual Agent | ||||||
| V* | 93.7 | 95.8 | -- | 67.0 | 90.1 | 94.7 |
| AndroidWorld | 64.2 | -- | -- | -- | -- | 70.3 |
* Empty cells (--) indicate scores not yet available or not applicable.