阿里千问开源 Qwen3.6-27B 稠密多模态模型，编码能力超越 Qwen3.5-397B-A17B

Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model | Qwen Blog Publication About Try Qwen Chat Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model April 22, 2026 · 8 min · 1646 words · Qwen Team | Translations: 简体中文 QWEN STUDIO HUGGING FACE MODELSCOPE DISCORD Following the launch of Qwen3.6-Plus and 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 , call via API on Alibaba Cloud Model Studio API (coming soon), or download weights from Hugging Face and ModelScope . 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 # 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 # 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 MMBench EN-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 # 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 and ModelScope for self-hosting, and through the Alibaba Cloud Model Studio API. You can also try it instantly on Qwen Studio . 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 # 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 # 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: """ 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 OpenAI import 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 trace answer_content = "" # Full response is_answering = False # Whether we have entered the answer phase print ( " \n " + "=" * 20 + "Reasoning" + "=" * 20 + " \n " )

for chunk in completion : if not chunk . choices : print ( " \n Usage:" ) 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

# Set your API key export DASHSCOPE_API_KEY =

# 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 # Qwen APIs also support the Anthropic API protocol, meaning you can use it with tools like Claude Code for elevated coding experience: # Install Claude Code npm install -g @anthropic-ai/claude-code

# Configure environment export 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 =