OpenMobile-2 releases open smartphone-agent models and training environment for more than 110 apps
Researchers from Nanjing University, SenseTime, and other universities released OpenMobile-2, including 9B and 27B models built on Qwen3.5 and Qwen3.6, together with the training environment, data, and reproduction instructions. The environment covers more than 110 simulated applications. The researchers reported that, with the same amount of training data, increasing app diversity raised success on unseen applications from 60.7% to 67.5%. They also reported that the 27B model scored 59.6 on SPA-Bench, the highest result among open models. These findings come from one secondary report and remain researcher-reported results.
OpenMobile-2 provides reproducible training resources for smartphone agents, and the researchers' controlled comparison suggests that training across more applications can improve performance on unseen apps more than adding examples from fewer apps when the data budget is fixed.
OpenMobile-2 proves open-source smartphone agents can rival proprietary models
Jonathan Kemper View the LinkedIn Profile of Jonathan Kemper
Oct 25, 2026
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Key Points
- The open-source OpenMobile-2 project trains AI smartphone agents in an environment with more than 110 simulated apps.
- App diversity matters more than data volume. With the same amount of training data, increasing the number of apps pushed the success rate on unseen applications from 60.7 to 67.5 percent.
- On real Android devices, the larger model scored 59.6 points on SPA-Bench, the highest result among open models.
#OpenMobile-2#OpenMobile-2-27B#Qwen3.5#Qwen3.6#Gemini 3.1 Pro#SPA-Bench#AndroidWorld#MobileWorld#Nanjing University#SenseTime