High-Immersion Persona & Unbounded Narrative Generation Engine
Tailored for Complex Roleplay, Deep Fiction, and Uninhibited Creative Writing
Crafted for passionate storytellers, writers, and open-source roleplay enthusiasts. Enjoy the journey!
--- # Qwen3.8-27B > [!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, TokenSpeed, etc. > [!Tip] > For users seeking managed, scalable inference without infrastructure maintenance, the official Qwen API service is provided by [Qwen Cloud](https://www.qwencloud.com). > In particular, **Qwen3.8-27B** will be available as a hosted version with more production features, e.g., 1M context length by default, official built-in tools. For more information, please refer to the [Qwen3.8-27B Overview](https://www.qwencloud.com/models/qwen3.8-27b). The service is coming soon. Stay tuned for updates. 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 via `preserve_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| 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 |
| 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 |
\boxed{}.” For the remaining models, we report the higher score from two prompt variants—one with and one without the \boxed{} formatting requirement.gpt-5.4-2026-03-05.