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Qwen/
Qwen3.8-2.4T-A95B
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Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of Qwen3.8 Max, with 95 billion active parameters out of 2.4 trillion total. It is suited for coding, research, complex reasoning, and agentic workflows.
Qwen3.8-2.4T-A95B
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Qwen3.8-2.4T-A95B
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.
For the first time, Qwen3.8 brings a Qwen-Max-class model to open release. Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Beyond answering harder questions, Qwen3.8 is designed to carry complex, multi-step tasks through to completion with greater reliability.
Qwen3.8 Highlights
Qwen3.8 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: Reasoning depth can be tuned with
reasoning_effort, and reasoning context from historical messages is retained viapreserve_thinking.
For more details, please refer to our blog post Qwen3.8-Max.
Model Overview
- Type: Causal Language Model
- Training Stage: Pre-training & Post-training
- Language Model
- Number of Parameters: 2.4T in total and 95B activated
- Hidden Dimension: 8192
- Token Embedding: 248,320 (Padded)
- Number of Layers: 92
- Hidden Layout: 23 × (3 × (Gated DeltaNet → MoE) → 1 × (Gated Attention → MoE))
- Gated DeltaNet:
- Number of Linear Attention Heads: 128 for V and 16 for QK
- Head Dimension: 128
- Gated Attention:
- Number of Attention Heads: 64 for Q and 4 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: 2048
- LM Output: 248,320 (Padded)
- MTP (Multi-Token Prediction): trained with multiple steps
- Context Length: 262,144 natively and extensible up to 1,010,000 tokens.
Benchmark Results
| Opus 4.8 | Fable 5 | GPT 5.6 Sol (max) | Qwen3.7-Max | Qwen3.8-Max | |
|---|---|---|---|---|---|
| Coding Agent | |||||
| Terminal Bench 2.1 | 84.6 | 84.6 | 88.8 | 74.5 | 86.6 |
| SWE-bench Pro | 69.2 | 80.0 | 64.6 | 60.6 | 67.7 |
| DeepSWE 1.1 | 59.0 | 70.0 | 73.0 | 21.6 | 56.6 |
| NL2Repo-Bench | 69.4 | -- | -- | 47.2 | 55.9 |
| FrontierSWE | 70.0 | 88.8 | -- | 40.7 | 73.5 |
| MLS-Bench-Lite | 42.8 | 49.9 | 46.2 | 31.7 | 41.0 |
| PaperBench | 80.3 | 88.8 | 90.5 | 64.8 | 93.0 |
| AndroidBench | 69.8 | 84.5 | 74.0 | 56.5 | 75.1 |
| QwenSWEBench | 84.0 | 86.3 | 73.5 | 63.4 | 80.7 |
| QwenQoderBench | 62.7 | 63.1 | 53.8 | 36.8 | 58.4 |
| QwenReactBench | 1694 | 1770 | 1564 | 1538 | 1724 |
| QwenSVGBench | 1648 | 1690 | 1758 | 1499 | 1713 |
| General Agent | |||||
| CoWorkBench | 72.3 | 75.9 | 71.5 | 64.6 | 74.8 |
| WorkSpaceBench | 66.8 | 68.7 | 65.6 | 61.4 | 67.7 |
| JobBench | 48.4 | 57.4 | 45.4 | 31.3 | 53.4 |
| SkillsBench | 65.1 | 70.9 | 73.5 | 61.2 | 70.2 |
| Agents' Last Exam (Pass / Score) | 27.0 / 45.1 | -- / -- | 30.6 / 53.6 | 11.8 / 31.1 | 27.0 / 52.4 |
| Automation-Bench (Pass@1) | 27.2 | 29.1 | 29.7 | 14.2 | 27.3 |
| Toolathlon Verified (Pass@1) | 76.2 | 77.9 | 74.9 | 49.7 | 72.5 |
| WideSearch | 72.9 | 81.2 | -- | 75.2 | 81.9 |
| HLE w/ tools | 57.9 | 64.5 | 58.0 | 53.5 | 56.2 |
| General Capabilities | |||||
| GPQA Diamond | 92.0 | 92.6 | 94.1 | 92.4 | 92.6 |
| HLE | 45.7 | 53.3 | 47.2 | 41.4 | 43.6 |
| IFBench | 62.2 | 63.5 | 72.7 | 79.1 | 82.8 |
| $OneMillion-Bench (expert score) | 41.8 | 55.9 | 53.8 | 44.4 | 52.5 |
| HealthBench | 52.4 | -- | 55.3 | 54.5 | 60.2 |
| PLawBench | 69.6 | 70.2 | 72.3 | 58.9 | 73.2 |
| PRBench-Legal | 52.7 | 57.6 | 57.6 | 48.5 | 57.6 |
| PRBench-Finance | 51.9 | 55.8 | 55.5 | 46.8 | 58.3 |
| MRCR v2 256K (8-needle) | 83.2 | -- | 93.8 | 86.7 | 92.9 |
| LongBench v2 | 69.1 | -- | 67.1 | 65.3 | 66.3 |
1. Fable5 results may involve fallbacks.
2. Terminal Bench 2.1: Evaluated with Claude Code (avg@10), using a 5-hour timeout and max_tokens=131,072. For all other models, we report the best published score across harnesses: Claude Opus 4.8 and Claude Fable 5 with Terminus 2 from Artificial Analysis (https://artificialanalysis.ai/evaluations/terminalbench-v2-1); GPT-5.6 Sol with Codex (https://openai.com/index/previewing-gpt-5-6-sol/).
3. SWE-bench Pro: Evaluated with the Claude Code harness, temp=1.0, top_p=0.95, and a 256K context window. Problematic tasks corrected and all baselines evaluated on the refined benchmark.
4. DeepSWE 1.1: Evaluated with the Claude Code and mini-SWE-agent harnesses, temp=1.0, top_p=0.95, and a 256K context window. We report the highest score among both harnesses; notably, Qwen3.8-Max performs best on Claude Code.
5. 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.
6. FrontierSWE: Evaluated with the Claude Code harness. All other available MEAN@5 results are taken from the official FrontierSWE leaderboard (https://www.frontierswe.com) as of August 3, 2026. Dominance scores are recomputed from the raw scores using the official evaluation script. "--" indicates that no official MEAN@5 result was available as of that date.
7. MLS-Bench-Lite: Evaluated with Claude Code using a 5-hour timeout and max_tokens=131,072. All other model scores are taken from the official leaderboard.
8. PaperBench: Evaluated in the BasicAgent setting under Code-Dev mode, judged by Claude Opus 4.6, and averaged over 3 runs (max 12 hours per run).
9. AndroidBench: Evaluated on the 95-task public subset, reporting avg@3 scores.
10. QwenSWEBench: Inhouse coding benchmark to evaluate 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-token context window.
11. QwenQoderBench: Inhouse coding benchmark to evaluate user experience on Qoder. Evaluated with the Claude Code harness. Reporting avg@5 with a 6-hour timeout, max_tokens=32,768, temperature=1.0, and a 256K-token context window.
12. QwenReactBench: Inhouse React project building benchmark using Claude Code as the harness, bilingual (EN/CN), 7 categories; auto-render + multimodal judge; BT/Elo rating.
13. QwenSVGBench: Inhouse SVG code generation benchmark; bilingual (EN/CN), auto-render + multimodal judge; BT/Elo rating.
14. CoWorkBench: Inhouse cowork benchmark for evaluating long-horizon tasks across computer science, finance, law, medical, and other productivity domains.
15. SkillsBench: Evaluated on the public SkillsBench v1.1 benchmark across 87 tasks, reporting the average score over three runs per task. Opus 4.8 and Fable 5 are evaluated on Claude Code; GPT-5.6 Sol is evaluated on Codex; the Qwen-series are evaluated on OpenCode. All results are from our own testing.
16. Automation-Bench: Evaluated on the 600-task public subset.
17. WideSearch: Evaluated with the Claude Code harness for external models and the Qwen-Agent harness for ours, reporting the average item-F1 over four runs.
18. $OneMillion-Bench: Evaluated using gemini-3.1-pro-preview.
19. PLawBench: Evaluated using gemini-3.1-pro-preview.
20. Empty cells (--): Scores are not yet available or are not applicable.
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