Alibaba launches qwen-ui-agent to enable models to truly 'use' every screen

Alibaba has officially released Qwen-UI-Agent, a GUI intelligent agent foundation model designed to operate effectively in real-world environments, including mobile devices, computers, web browsers, and DeepSearch. The model demonstrates strong performance across multiple benchmarks, surpassing leading models in several categories. On mobile tasks, Qwen-UI-Agent achieved 82.1% on MobileWorld, outperforming GPT-5.6 Sol, Claude Opus 4.8, Seed 2.1 Pro by 12.0, 14.6, and 8.9 percentage points respectively. It also scored 92.2% on MobileWorld-Real, surpassing Gemini 3.1 Pro, Claude Opus 4.8, GPT-5.6 Sol, and Seed 2.1 Pro. On Android Daily, it reached 97.5%, nearly perfect. On desktop tasks, it scored 79.5% on OSWorld-Verified, outperforming GPT-5.5, Gemini 3.1 Pro, and Seed 2.1 Pro. It also achieved 40.0% on OSWorld-v2 Partial, with a 58% reduction in execution steps compared to the baseline. In web and DeepSearch tasks, it scored 73.6% on WebArena, the highest among all compared models, and 75.0% on BrowseComp-ZH. In GUI grounding tasks, it achieved 81.5% on ScreenSpot-Pro, setting new state-of-the-art records on four other benchmarks. The model also excels in general and agentic capabilities, outperforming both training base models and GUI-specific models. It operates in a real-world environment with over 100 real phones and 150 apps, using its own MobileWorld-Real benchmark with 400+ tasks and 100+ apps. The model is designed to handle complex, long-tail tasks in real-world scenarios. It integrates safety checks throughout task execution, refusing to perform any interface actions for illegal or high-risk requests and pausing for user confirmation in sensitive operations such as payments, data deletion, and privacy authorization. Additionally, it supports command-line operations and batched actions, with nearly half of the actions on desktop tasks being batched, significantly improving efficiency. The model can also perform online reinforcement learning over more than 100 steps, with approximately 10,000 concurrent environments running simultaneously. The project is available at https://tongyi-mai.github.io/Qwen-UI-Agent and on GitHub at https://github.com/Tongyi-MAI/MAI-UI.