Alibaba Tongyi releases OpenGCUA, an open computer-use agent stack: code-first harness lifts all eight frontier models on OSWorld-v2 (mean 45.3→64.9, 53% fewer steps), with trajectories released

Why it matters

【Key points】

Alibaba Tongyi's MAI-UI team released the first version of OpenGCUA (Open Generalist CUA) on October 15: an open stack for "generalist computer-use agents." The claim is that computer use should not be just screenshots and clicks: the model should decide what to observe (screenshot, accessibility tree, command output), how to act (click, call an API, run shell, or write a Python program with loops and checks that combines them), and how to track task state.

They ran eight frontier models in the same harness (GPT-6 Astra, Claude Opus 5, Gemini 3.8 Flash, Qwen3.8-Max/Flash, Kimi K3, DeepSeek-V4.1-Flash, GLM-5.3-Flash). On the 82-task OSWorld-v2 offline set, versus a batched GUI-only baseline, mean partial score rose from 45.3 to 64.9 (+19.6) and steps per task fell 53% on average; GPT-6 Astra went from 102 to 34 steps and scored highest (75.7); GLM-5.3-Flash gained most (+35.2). On four real macOS tasks, Qwen3.8-Max used 19.4x GPT-6 Astra's reasoning tokens and finished 3 of 4 vs 4 of 4. No new model yet: the release is the harness and prompts, trajectories for all eight models, a preliminary macOS harness and a cross-device SDK (Apache 2.0).

【Verifiable】

Paper: https://arxiv.org/abs/2610.22200

GitHub: https://github.com/Tongyi-MAI/OpenGCUA

Project: https://tongyi-mai.github.io/OpenGCUA/

【Our read】

1. A rare open dissection of GPT-6 Astra-style computer use with released trajectories; the like-for-like cost and step comparison across eight models is the most useful part.

2. Same model, code-first harness, clearly better results; weaker models gain most and the spread halves.

3. The real-device part is four tasks, one run per model; the authors say it is not a ranking.