Qwen/Qwen3.8-2.4T-A95B

Qwen3.8-2.4T-A95B

> [!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 vLLM, SGLang, TokenSpeed, etc.

> [!Tip] > For users seeking managed, scalable inference without infrastructure maintenance, the official Qwen API service is provided by Qwen Cloud. > > In particular, Qwen3.8-Max is the official version based on Qwen3.8-2.4T-A95B with more features, such as vision input & non-thinking support, 1M context length by default, official built-in tools, etc. > For more information, please refer to the Qwen3.8-Max Overview.

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 via preserve_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 4.8Fable 5GPT 5.6 Sol (max)Qwen3.7-MaxQwen3.8-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

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