Alibaba's open agent stack lifted all 8 AI models it tested
Why it matters
Most computer-use agents look at a screenshot, click and repeat, while fixed rules set by developers decide what they see and remember. Alibaba researchers' OpenGCUA lets the model write code that mixes GUI clicks, accessibility-tree actions, shell commands and API calls, so each result can decide the next step, and moves decisions about what to observe (even whether to take a new screenshot) from fixed harness rules to the model. On OSWorld-v2, vs a GUI-only baseline with batched actions, the mean partial score across 8 frontier models rose from 45.3 to 64.9 (+19.6 points) with 53% fewer steps per task on average. GPT-6 Astra, the top scorer, went from 67.1 to 75.7 and cut steps per task from 102.1 to 33.5; Claude Opus 5 gained 9.4 points and cut output tokens per task from 378.3K to 89.3K. The biggest gains came from the lowest starters: GLM-5.3-Flash +35.2, DeepSeek-V4.1-Flash +29.8. Code and prompts are open under Apache 2.0, while frontier computer-use systems remain largely closed.
Read the source: arXiv (Alibaba Group) (opens in a new tab)
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