Qwen3.8 27B: Dense Vision-Language Model with Flexible Thinking Control

Qwen3.8 27B is a dense multimodal foundation model from Alibaba's Qwen team, described by its authors as part of "the most capable generation in the Qwen open-model family to date." Built on the Qwen3.5 hybrid Gated DeltaNet and Gated Attention architecture, it is a native vision-language model that understands images and videos while offering per-request control over how much it reasons before answering. At 27 billion parameters it targets coding, professional work, research, and long-horizon agentic tasks.

Key Features

• Broad Capability Gains - Comprehensive improvements across coding, professional work, research, and long-horizon agentic tasks
• Agent Execution - Stronger autonomous planning and better handling of environment feedback, designed to carry complex multi-step tasks through to completion
• Flexible Thinking Control - Thinking is on by default and can be disabled per request; reasoning depth is tunable across xhigh, medium, and low, and prior reasoning can be carried forward between turns
• Vision-Language Understanding - Spans STEM diagrams and documents through to hour-scale video comprehension
• Downstream Compatibility - Broader support for popular agent harnesses and development tools
• Long Context - 262,144 tokens natively, extensible up to 1,000,000 tokens with RoPE scaling such as YaRN

Architecture

• Causal Language Model with Vision Encoder
• 27B dense parameters
• 64 layers with a 16 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN)) hybrid layout
• Gated DeltaNet linear attention (48 V heads, 16 QK heads, head dim 128)
• Gated Attention (24 Q heads, 4 KV heads, head dim 256, rotary dim 64)
• Hidden dimension 5120, Feed Forward Network intermediate dimension 17408
• Multi-token prediction (MTP) trained with multiple steps
• Native 262K context, extensible to 1M tokens

Use Cases

• Coding and software engineering agents
• Professional and office workflows carried across many steps
• Computer, browser, and mobile control agents
• Document, chart, and STEM diagram analysis
• Visual web development and application recreation
• Long-video comprehension and long-context research over large document sets

Benchmarks

On the Qwen3.8 benchmark suite (source), Qwen3.8 27B scores SWE-bench Pro 61.7, QwenSWEBench 79.0, LiveCodeBench v6 90.3, GPQA Diamond 89.2, IFBench 79.5, CoWorkBench 70.7, and HLE 30.8 on text tasks. On multimodal tasks it scores OSWorld-Verified 84.3, AndroidWorld 81.9, WebArena-Verified 64.8, Vision2Web 62.9, MathVision 94.6, CharXiv (RQ) 90.2, and BabyVision 85.6. Several of these are in-house evaluations from the Qwen team: CoWorkBench covers long-horizon productivity domains, QwenSWEBench targets software engineering ability, and RecreationBench evaluates application recreation across desktop, mobile, and web.