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Published as a conference paper at NeurIPS 2026

OS-Omni: A Cross-Platform Benchmark for Generalist Computer-Using Agents

Hui Shen, Yunta Hsieh, Jianing Ma, Ziyuan Liu, Qi Han, Xiuqi Xu, Yanheng shang, Chenqi Yin, Zesen Zhao, Boyuan Zheng, Jisen Li, Xiaoxia Wu, Ruoyan Zhang, Dongfu Jiang, Ruiyao Liu, Jing Xiong, He Xiao, Nan Zheng, Hangxiang Chen, Shengyang Tao, Deyang Luorong, Jingxuan Zhang, Chaofan Tao, Yuren Wang, Jiaqi Mo, Hei Ting Chan, Sicheng Chen, Kai Zhang, Mengkang Hu, Zhongwei Wan, Xin Wang, Chuanyang Zheng, Ziheng Zhang, Hengyuan Zhang, Zunhai Su, Kanzhi Cheng, Jiawei Lu, Yifan Zhang, Shen Yan, Ben Athiwaratkun, Qiushi Sun, Mi Zhang, Ping Luo, Wenhu Chen, Ngai Wong

Abstract

Computer-using agents are increasingly expected to complete realistic user goals rather than isolated GUI actions. However, existing evaluations are fragmented across web, mobile, and desktop settings, making it difficult to measure whether the same agent can sustain long-horizon workflows across heterogeneous software platforms while preserving final-state correctness. We introduce OS-Omni, a 1,200-task cross-platform benchmark and execution environment for evaluating computer-using agents across Windows, Ubuntu, macOS, iOS, Android, and Web environments. OS-Omni provides reproducible initialization, platform-specific adapters, trajectory logging, and executable final-state evaluators, and includes controlled service-backed applications for workflows involving account, cart, budget, calendar, message, and other persistent state. Human studies show that OS-Omni tasks require 67.54 interaction steps, substantially exceeding OSWorld references of 15.0 steps. Throughout our experiments, the strongest evaluated agent, GPT-5.5, reaches 47.1% average success, trailing the 88.1% human reference by 41.0 points; on hard workflows, it drops from 77.8% success on easy tasks to 16.4%, while humans remain at 76.3%. These results indicate that current agents remain far from platform-robust, long-horizon computer use, especially when success requires sustained state tracking and durable final-state changes.

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