Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model

 Qwen Studio More EN DownloadTry Qwen Studio Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model | Qwen [](https://qwen.ai/ "Qwen \(Alt + H\)") * [Blog](https://qwen.ai/blog/ "Blog") * [Publication](https://qwen.ai/publication "Publication") * [About](https://qwen.ai/about "About") * [Try Qwen Chat ](https://chat.qwen.ai "Try Qwen Chat") # Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model 2026/04/22 · 21 minute · 4226 words · QwenTeam丨Translations:简体中文  [QWEN STUDIO](https://chat.qwen.ai)[HUGGING FACE](https://huggingface.co/Qwen/Qwen3.6-27B)[MODELSCOPE](https://modelscope.cn/models/Qwen/Qwen3.6-27B)[DISCORD](https://discord.gg/yPEP2vHTu4) Following the launch of [Qwen3.6-Plus](https://qwen.ai/blog?id=qwen3.6) and [Qwen3.6-35B-A3B](https://qwen.ai/blog?id=qwen3.6-35b-a3b), we are excited to open-source **Qwen3.6-27B** — a dense 27-billion-parameter multimodal model at the scale the community has been asking for most. Still supporting both multimodal thinking and non-thinking modes, Qwen3.6-27B delivers flagship-level agentic coding performance, **surpassing the previous-generation open-source flagship Qwen3.5-397B-A17B** (397B total / 17B active MoE) across all major coding benchmarks. As a dense architecture, it is straightforward to deploy without MoE routing complexity, making it an ideal choice for developers who need top-tier coding capabilities at a practical, widely-deployable scale. Qwen3.6-27B is now live on Qwen Studio, available through our API, and released as open weights for the community. * **Qwen3.6-27B** is a fully open-source dense model (27B parameters), featuring: * flagship-level agentic coding that surpasses Qwen3.5-397B-A17B * strong text and multimodal reasoning ability * You can chat interactively on [Qwen Studio](https://chat.qwen.ai), call via API on [Alibaba Cloud Model Studio API](https://modelstudio.alibabacloud.com/) (coming soon), or download weights from [Hugging Face](https://huggingface.co/Qwen/Qwen3.6-27B) and [ModelScope](https://modelscope.cn/models/Qwen/Qwen3.6-27B).  ## Performance[#](https://qwen.ai/blog?id=qwen3.6-27b#performance) Below we present comprehensive evaluations of Qwen3.6-27B against both dense and MoE baselines, including our previous-generation open-source flagship Qwen3.5-397B-A17B. Qwen3.6-27B delivers remarkable improvements across agentic coding benchmarks, surpassing models with up to 15x its total parameter count. ### Language[#](https://qwen.ai/blog?id=qwen3.6-27b#language) Qwen3.6-27B achieves a breakthrough in agentic coding for dense models. With only 27B parameters, it outperforms the Qwen3.5-397B-A17B (397B total / 17B active) on every major coding benchmark — including SWE-bench Verified (77.2 vs. 76.2), SWE-bench Pro (53.5 vs. 50.9), Terminal-Bench 2.0 (59.3 vs. 52.5), and SkillsBench (48.2 vs. 30.0). It also surpasses all peer-scale dense models by a wide margin. On reasoning tasks, Qwen3.6-27B achieves 87.8 on GPQA Diamond, competitive with models several times its size. | | 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. ### Vision Language[#](https://qwen.ai/blog?id=qwen3.6-27b#vision-language) Qwen3.6-27B is natively multimodal, supporting both vision-language thinking and non-thinking modes in a single unified checkpoint — the same as Qwen3.6-35B-A3B. It handles images and video alongside text, enabling multimodal reasoning, document understanding, and visual question answering. | | 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. ## Build with Qwen3.6-27B[#](https://qwen.ai/blog?id=qwen3.6-27b#build-with-qwen36-27b) Qwen3.6-27B is coming soon to Alibaba Cloud Model Studio. Please stand by until we are fully ready. Qwen3.6-27B is available as open weights on [Hugging Face](https://huggingface.co/Qwen/Qwen3.6-27B) and [ModelScope](https://modelscope.cn/models/Qwen/Qwen3.6-27B) for self-hosting, and through the [Alibaba Cloud Model Studio](https://modelstudio.alibabacloud.com/) API. You can also try it instantly on [Qwen Studio](https://chat.qwen.ai). The model can be seamlessly integrated with popular third-party coding assistants, including OpenClaw, Claude Code, and Qwen Code, to streamline development workflows and enable efficient, context-aware coding experiences. ### API Usage[#](https://qwen.ai/blog?id=qwen3.6-27b#api-usage) This release supports the `preserve_thinking` feature: preserving thinking content from all preceding turns in messages, which is **recommended for agentic tasks**. #### Alibaba Cloud Model Studio[#](https://qwen.ai/blog?id=qwen3.6-27b#alibaba-cloud-model-studio) Alibaba Cloud Model Studio supports industry-standard protocols, including chat completions and responses APIs compatible with OpenAI’s specification, as well as an API interface compatible with Anthropic. Example code for chat completions API is provided below: ``` python __ """ Environment variables (per official docs): DASHSCOPE_API_KEY: Your API Key from https://modelstudio.console.alibabacloud.com DASHSCOPE_BASE_URL: (optional) Base URL for compatible-mode API. - Beijing: https://dashscope.aliyuncs.com/compatible-mode/v1 - Singapore: https://dashscope-intl.aliyuncs.com/compatible-mode/v1 - US (Virginia): https://dashscope-us.aliyuncs.com/compatible-mode/v1 DASHSCOPE_MODEL: (optional) Model name; override for different models. """from openai import OpenAIimport os api_key = os.environ.get("DASHSCOPE_API_KEY")if not api_key: raise ValueError( "DASHSCOPE_API_KEY is required. " "Set it via: export DASHSCOPE_API_KEY='your-api-key'" ) client = OpenAI( api_key=api_key, base_url=os.environ.get( "DASHSCOPE_BASE_URL", "https://dashscope-intl.aliyuncs.com/compatible-mode/v1", ),) messages = [{"role": "user", "content": "Introduce vibe coding."}] model = os.environ.get( "DASHSCOPE_MODEL", "qwen3.6-27b",)completion = client.chat.completions.create( model=model, messages=messages, extra_body={ "enable_thinking": True, # "preserve_thinking": True, }, stream=True) reasoning_content = "" # Full reasoning traceanswer_content = "" # Full responseis_answering = False # Whether we have entered the answer phaseprint("\n" + "=" * 20 + "Reasoning" + "=" * 20 + "\n") for chunk in completion: if not chunk.choices: print("\nUsage:") print(chunk.usage) continue delta = chunk.choices[0].delta # Collect reasoning content only if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None: if not is_answering: print(delta.reasoning_content, end="", flush=True) reasoning_content += delta.reasoning_content # Received content, start answer phase if hasattr(delta, "content") and delta.content: if not is_answering: print("\n" + "=" * 20 + "Answer" + "=" * 20 + "\n") is_answering = True print(delta.content, end="", flush=True) answer_content += delta.content ``` For more information, please visit the [API doc](https://modelstudio.console.alibabacloud.com/?tab=doc#/doc/?type=model&url=2840915). ### Coding & Agents[#](https://qwen.ai/blog?id=qwen3.6-27b#coding--agents) Qwen3.6-27B features excellent agentic coding capabilities and can be seamlessly integrated into popular third-party coding assistants, including OpenClaw, Claude Code, and Qwen Code. #### OpenClaw[#](https://qwen.ai/blog?id=qwen3.6-27b#openclaw) Qwen3.6-27B is compatible with [OpenClaw](https://openclaw.ai) (formerly Moltbot / Clawdbot), a self-hosted open-source AI coding agent. Connect it to [Model Studio](https://www.alibabacloud.com/help/en/model-studio/openclaw) to get a full agentic coding experience in the terminal. Get started with the following script: ``` bash __ # Node.js 22+curl -fsSL https://molt.bot/install.sh | bash # macOS / Linux # Set your API keyexport DASHSCOPE_API_KEY= # Launch OpenClawopenclaw dashboard # web browser# openclaw tui # Open a new terminal and start the TUI ``` On first use, edit `~/.openclaw/openclaw.json` to point OpenClaw at Model Studio. Find or create the following fields and merge them — **do not overwrite the entire file** to preserve your existing settings: ``` json __ { "models": { "mode": "merge", "providers": { "modelstudio": { "baseUrl": "https://dashscope-intl.aliyuncs.com/compatible-mode/v1", "apiKey": "DASHSCOPE_API_KEY", "api": "openai-completions", "models": [ { "id": "qwen3.6-27b", "name": "qwen3.6-27b", "reasoning": true, "input": ["text", "image"], "contextWindow": 131072, "maxTokens": 16384 } ] } } }, "agents": { "defaults": { "model": { "primary": "modelstudio/qwen3.6-27b" }, "models": { "modelstudio/qwen3.6-27b": {} } } }} ``` #### Qwen Code[#](https://qwen.ai/blog?id=qwen3.6-27b#qwen-code) Qwen3.6-27B is compatible with [Qwen Code](https://qwen.ai/qwencode), an open-source AI agent designed for the terminal and deeply optimized for the Qwen Series. Get started with the following script: ``` bash __ # Node.js 20+npm install -g @qwen-code/qwen-code@latest # Start Qwen Code (interactive)qwen # Then, in the session:/help /auth ``` On first use, you’ll be prompted to sign in. You can run `/auth` anytime to switch authentication methods. #### Claude Code[#](https://qwen.ai/blog?id=qwen3.6-27b#claude-code) Qwen APIs also support the Anthropic API protocol, meaning you can use it with tools like **Claude Code** for elevated coding experience: ``` bash __ # Install Claude Codenpm install -g @anthropic-ai/claude-code # Configure environmentexport ANTHROPIC_MODEL="qwen3.6-27b"export ANTHROPIC_SMALL_FAST_MODEL="qwen3.6-27b"export ANTHROPIC_BASE_URL=https://dashscope-intl.aliyuncs.com/apps/anthropic export ANTHROPIC_AUTH_TOKEN= # Launch the CLIclaude ``` ## Summary[#](https://qwen.ai/blog?id=qwen3.6-27b#summary) Qwen3.6-27B demonstrates that a well-trained dense model can surpass much larger predecessors on the tasks that matter most for developers. At 27 billion parameters — the most widely deployed open-source scale — it outperforms the 397B-parameter Qwen3.5-397B-A17B on every major agentic coding benchmark, while remaining straightforward to deploy and serve. With Qwen3.6-27B joining the roster, the Qwen3.6 open-source family now offers a comprehensive range of models, underscoring a generation where agentic coding achieved breakthroughs across every scale — from the 3B-active Qwen3.6-35B-A3B to the API-accessible Qwen3.6-Plus and Qwen3.6-Max-Preview. We are grateful for the community’s feedback and look forward to seeing what you build with these models. Stay tuned for more from the Qwen team! ## Citation[#](https://qwen.ai/blog?id=qwen3.6-27b#citation) Feel free to cite the following article if you find Qwen3.6-27B helpful: ``` bibtex __ @misc{qwen36_27b, title = {{Qwen3.6-27B}: Flagship-Level Coding in a 27B Dense Model}, url = {https://qwen.ai/blog?id=qwen3.6-27b}, auth