Qwen/Qwen3.8-2.4T-A95B

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.

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.

: 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.

: 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.

: 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 .

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