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Qwen/
Qwen3.8-27B
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Qwen3.8 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be enabled or disabled.
Qwen3.8 27B
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Model Information
Following the widespread community adoption of the Qwen3.5 and Qwen3.6 series, we are pleased to introduce Qwen3.8, the most capable generation in the Qwen open-model family to date.
Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Qwen3.8-27B brings these advances to a compact, deployment-friendly dense model: a native vision-language model that understands images and videos, with flexible thinking control, designed to carry complex, multi-step tasks through to completion with greater reliability.
Qwen3.8 Highlights
Qwen3.8-27B features the following enhancements:
- Core Capabilities: Comprehensive improvements across coding, professional work, research, and long-horizon agentic tasks.
- Agent Execution: Stronger autonomous planning and better handling of environment feedback, leading to more reliable end-to-end task completion.
- Downstream Compatibility: Broader support for popular harnesses and development tools, making it easier to integrate into your existing stack.
- Flexible Thinking Control: Thinking mode is on by default and can be disabled per request; reasoning depth can be tuned with
reasoning_effort, and reasoning context from historical messages is retained viapreserve_thinking. - Vision-Language Understanding: Native support for image and video understanding, from STEM diagrams and documents to hour-scale videos.
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: 248,320 (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: 17,408
- LM Output: 248,320 (Padded)
- MTP (Multi-Token Prediction): trained with multiple steps
- Context Length: 262,144 natively and extensible up to 1,000,000 tokens.
Benchmark Results
Text Performance
.vl-table th{font-size:15px!important;line-height:1.2} .vl-table td:not(.benchmark-cell):not([colspan]){font-size:15px;line-height:1.2;vertical-align:middle} .vl-table .benchmark-cell{padding:12px 10px 12px 18px!important;vertical-align:middle} .vl-table .benchmark-capability{font-size:15px;font-weight:600;line-height:1.22;color:#171717} .vl-table .benchmark-name{margin-top:4px;font-size:11px;font-weight:400;line-height:1.2;color:#6B6B6B} .vl-table .metric-stack{display:flex;flex-direction:column;gap:7px;padding:3px 0} .vl-table .metric-label{font-size:10px;font-weight:400;line-height:1.1;color:#777} .vl-table .metric-value{margin-top:2px;font-size:15px;line-height:1.15;color:#171717}| Qwen3.8-27B | Qwen3.6-27B | Qwen3.7-Plus | Muse Glimmer-30B | Opus4.6 Max | |
|---|---|---|---|---|---|
| Coding | |||||
Agentic terminal coding Terminal Bench 2.1 (Terminus) | 73.0 | 63.4 | 64.0 | 51.7 | 78.2 |
Agentic coding SWE-bench Pro | 61.7 | 53.5 | 57.6 | 51.2 | 53.4 |
Repo-level code generation NL2Repo-Bench | 42.3 | 36.2 | 41.1 | -- | 47.6 |
Agentic coding DeepSWE 1.1 | 42.2 | 13.3 | 14.2 | -- | -- |
Software engineering QwenSWEBench | 79.0 | 49.3 | 59.2 | -- | 63.8 |
| Agent | |||||
Long-horizon office work CoWorkBench | 70.7 | 61.0 | 65.1 | -- | 68.2 |
Professional job tasks JobBench | 33.4 | 21.8 | 27.6 | -- | -- |
Frontier agentic tasks Agents' Last Exam | Pass@1 20.4 Score 42.9 | Pass@1 10.6 Score 27.3 | Pass@1 13.2 Score 33.6 | -- | -- |
| General | |||||
Instruction following IFBench | 79.5 | 69.1 | 79.1 | 77.0 | 62.5 |
Scientific reasoning GPQA Diamond | 89.2 | 87.8 | 90.3 | 83.5 | 91.3 |
Multidisciplinary reasoning HLE | 30.8 | 24.0 | 34.7 | 22.0 | 40.0 |
Competitive coding LiveCodeBench v6 | 90.3 | 83.9 | 89.6 | -- | 88.8 |
- SWE-bench Pro: Except for Opus4.6 Max, which uses the officially reported score, all models are evaluated with the Claude Code harness at temp=1.0, top_p=0.95, and a 256K context window. Problematic tasks were corrected, and all baseline models were re-evaluated on the refined benchmark.
- NL2Repo-Bench: Evaluated with the Claude Code harness. To prevent reward hacking, we disable Bash commands that attempt to access the specific repository, such as pip download, pip install, and git clone.
- DeepSWE 1.1: Evaluated with the Claude Code harness at temp=1.0, top_p=0.95, and a 256K context window.
- QwenSWEBench: In-house coding benchmark for evaluating models' software engineering capabilities. Evaluated with the Claude Code harness. Reporting avg@3 with an 8-hour timeout, max_tokens=32,768, temperature=1.0, and a 256K context window.
- CoWorkBench: In-house cowork benchmark for evaluating long-horizon tasks across computer science, finance, law, medical, and other productivity domains.
- HLE: Judged by GPT-4o.
- The best result in each row is shown in bold.
- Empty cells (--) indicate that results are not yet available or not applicable.
VL Performance
| Qwen3.8-27B | Qwen3.6-27B | Qwen3.7-Plus | Muse Glimmer-30B | Opus4.6 Max | |
|---|---|---|---|---|---|
| Agentic Multimodal Intelligence | |||||
Computer use OSWorld-Verified | 84.3 | 63.9 | 73.3 | 65.9 | 72.7 |
Browser use WebArena-Verified | 64.8 | 48.8 | 55.3 | -- | -- |
Mobile use AndroidWorld | 81.9 | 70.3 | 81.0 | -- | 62.0 |
Application recreation RecreationBench | 47.1 | 29.8 | 30.2 | -- | -- |
Multimodal tool use ClawEval-MM | Pass@3 57.4 Average 56.9 | Pass@3 42.6 Average 50.4 | Pass@3 57.4 Average 60.1 | -- | Pass@3 52.5 Average 54.7 |
Multimodal software engineering SWE-MM | 38.6 | 25.7 | 30.0 | -- | 27.1 |
Visual web development Vision2Web | 62.9 | 45.0 | 42.1 | -- | -- |
| General Multimodal Intelligence | |||||
Visual math problem solving MathVision | Without CI 90.0 With CI 94.6 | Without CI 85.1 | Without CI 90.3 | -- | Without CI 65.5 |
General visual reasoning BabyVision | Without CI 65.7 With CI 85.6 | Without CI 28.9 | Without CI 64.7 With CI 70.4 | -- | Without CI 12.6 |
Scientific chart analysis CharXiv (RQ) | Without CI 83.7 With CI 90.2 | Without CI 78.4 | Without CI 85.8 With CI 85.9 | 78.8 | Without CI 66.0 |
Document intelligence OmniDocBench 1.5 | 91.1 | 89.4 | 91.4 | 75.8 | 86.6 |
Real-world perception RealWorldQA | 85.9 | 84.1 | 86.9 | -- | 73.9 |
Embodied intelligence ERQA | 65.5 | 62.5 | 69.8 | -- | 40.8 |
- MathVision, BabyVision, and CharXiv (RQ): Where both settings are available, cells report “Without CI” and “With CI” separately; otherwise, only the available setting is shown. A small number of incorrect ground-truth annotations in MathVision and CharXiv (RQ) were corrected following manual verification, and all reported scores on those benchmarks were computed using the corrected annotations.
- MathVision: Qwen3.8-27B is evaluated using the fixed prompt: “Please reason step by step, and put your final answer within
\boxed{}.” For the remaining models, we report the higher score from two prompt variants—one with and one without the\boxed{}formatting requirement. - WebArena-Verified: Scores are computed with the official WebArena-Verified grader under the OSWorld scaffold.
- RecreationBench: An in-house, long-horizon application-recreation benchmark designed to evaluate hybrid-agent capabilities across five platforms: desktop (Ubuntu, macOS, and Windows), mobile (Android), and the web.
- ClawEval-MM: Scores are reported as “Pass@3 / average score.” Pass@3 is the percentage of tasks passed in at least one of three trials; the average score is the mean benchmark score across the three trials.
- Vision2Web: Scores are averaged across the frontend, webpage, and website categories. Evaluations use the Claude Code harness and are judged by
gpt-5.4-2026-03-05. - SWE-MM: Scores are evaluated on the Claude Code harness using the public dev split of SWE-bench Multimodal, with the modifications described in Appendix 8.3 of the Claude Opus 4.7 system card.
- Empty cells (--) indicate that results are not yet available or not applicable.
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