MacArena: Benchmarking Computer Use Agents on an Online macOS Environment

Researchers introduce MacArena, a comprehensive benchmark with 421 tasks across 50 macOS applications to evaluate computer-use agents on Apple's native platform. The benchmark reveals significant performance gaps between Linux-based benchmarks and macOS environments, with leading AI models showing over 26% performance degradation on macOS-native tasks, indicating that existing evaluations may overestimate cross-platform GUI competence.

MacArena addresses a critical gap in AI agent benchmarking by providing the first comprehensive evaluation framework for macOS environments running natively on Apple Silicon. Previous benchmarks like OSWorld and the limited macOSWorld focused on Linux systems or x86 virtual machines, allowing researchers to potentially overestimate agent capabilities by testing on familiar operating systems. The introduction of 421 manually verified tasks spanning diverse applications reveals that macOS presents unique GUI challenges requiring specialized agent development.

The benchmark's findings carry substantial implications for AI development. Model rankings inverting between ported and native macOS tasks demonstrates that strong performance on existing benchmarks reflects dataset familiarity rather than genuine architectural robustness. This pattern suggests current computer-use agents lack the cross-platform generalization necessary for real-world deployment across diverse computing environments. The 26% performance gap for leading models highlights how platform-specific GUI conventions, accessibility patterns, and interaction modalities can significantly impact agent effectiveness.

For developers and organizations building AI agents, MacArena establishes a new standard for rigorous evaluation. Companies investing in agent technology must now account for platform-specific optimization requirements, potentially increasing development complexity and costs. The benchmark enables more honest assessment of agent capabilities before production deployment, reducing the risk of disappointing real-world performance.

Looking forward, MacArena will likely drive focused research into platform-agnostic GUI understanding and control mechanisms. Researchers may develop agents specifically optimized for macOS workflows, while others pursue genuinely cross-platform solutions. The benchmark's native Apple Silicon compatibility positions it as the definitive macOS evaluation framework, similar to OSWorld's role for Linux environments.

• →MacArena provides 421 manually verified tasks across 50 macOS applications on native Apple Silicon, addressing a significant benchmarking gap.
• →Leading AI models show 26% performance degradation on macOS-native tasks compared to Linux benchmarks, suggesting previous evaluations overstated cross-platform capabilities.
• →Model performance rankings invert between ported and native macOS tasks, revealing that existing benchmark success reflects dataset familiarity rather than genuine GUI competence.
• →macOS presents distinct GUI challenges including unique accessibility patterns and interaction modalities not captured by Linux-based evaluation frameworks.
• →The benchmark will likely drive specialized agent development for macOS while establishing standards for more rigorous cross-platform AI evaluation.