Alibaba's Qwen team has officially released Qwen 3.8-Max, the most capable model in the Qwen family to date, marking a significant leap in autonomous AI capabilities. With 2.4 trillion parameters (95B active), the model is built on the architectural foundation of Qwen 3.5 and delivers comprehensive improvements across coding, work, research, and long-horizon tasks.
In a notable first, the company will open-source the weights of a Qwen-Max-class model, with the release scheduled for next week. This move is expected to accelerate research and development in the AI community, allowing developers to build upon a state-of-the-art model.
The model's autonomy was showcased in three demanding challenges. In one test, Qwen 3.8-Max autonomously built a self-evolving harness for the 'oh-my-cli' project over a 10+ day run, accumulating 265 commits, 127 PRs, and 151 issues in a GitHub repository. The harness integrated user feedback, community practices, and self-test results into a continuous engineering loop.
In another challenge, the model reproduced a research paper on data selection for LLM reasoning from scratch, writing ~7,600 lines of code and running 33 rounds of GPU training over ~125 hours. It not only replicated the paper's findings but also evolved a new method that outperformed the original by +2.7 points on the AIME24 benchmark.
Perhaps most impressively, Qwen 3.8-Max entered a real online contest—the WWW2025 Multimodal Dialogue Intent Recognition Challenge—and within a strict 24-hour limit, built a full solution that beat 458 of 526 human teams (87% of the field), achieving an accuracy of 0.853.
The model's real-world work capabilities are enhanced by scaling RL environments and compute, a universal reward system, and an online data balancer. These innovations ensure stable, continued scaling of RL training, leading to consistent gains across dozens of benchmarks.