The Shanghai Artificial Intelligence Laboratory has unveiled the full version of its agent-oriented large model, Atria Dawn. This release occurred approximately one month after the preliminary version of Atria Dawn was introduced in mid-September.
The model was trained on the GLM-5.2 base model, which contains 744 billion parameters in a mixture-of-experts format. It is designed to support scientific research and engineering work that requires environmental understanding, tool usage, and multi-step task execution.
The main change noted by the laboratory is the increase in the context window from 256 thousand tokens available in the preview to 512 thousand tokens in the full version. However, the model download table in the project repository on GitHub still lists 256K for both the full model and its FP8 version, so developers are advised to consult the hosted API documentation for the applicable limit.
The laboratory now classifies the model's capabilities into four areas. Discovery involves data collection and systematization, conducting in-depth research, and transforming research questions into executable experiment plans. Creation covers software development, interactive applications, games, machine learning systems, and data visualizations. Delivery allows technical specifications, data, and design requirements to be converted into structured reports and presentations. Cybersecurity focuses on analyzing security issues, vulnerability checking, patch application, and retesting, limited to authorized environments.
Compared to the preview, the laboratory highlights significant improvements: a gain of 7.2 points in JobBench (57.5 vs 50.3), an increase of 2.7 points in WideSearch (84.6 vs 81.9), and an increase of 2.6 points in PaperBench (89.6 vs 87.0). However, the evaluation table in the repository, which the laboratory claims corresponds to its website, shows smaller improvements across most other benchmarks, including AutomationBench (54.7), SkillsBench (67.8), and SWE-bench Pro (61.7). In some tests, the preview showed equal or higher results, such as in BrowseComp, where the preview's 92.5 surpasses the full version's 91.3.
The model is available via hosted APIs with separate international and Chinese endpoints. The repository includes setup guides for SGLang, vLLM, and Codex. The model accepts only text input. The code is released under the MIT license, but the weights for the full version on Hugging Face and ModelScope are currently marked as 'Coming Soon'; only the preview weights are available for download at this time.
