A Survey on Benchmarks of LLM-based GUI Agents

LLM-based GUI agents have made rapid progress in understanding visual interfaces, interpreting user intentions, and executing multi-step operations across web, mobile, and desktop environments. As these agents become more capable, systematic and reproducible evaluation has become essential for measuring progress and identifying remaining weaknesses. This survey provides a comprehensive overview of benchmarks for LLM-based GUI agents, covering three major categories: grounding and QA tasks, navigation and multi-step reasoning tasks, and open-world environments that reflect realistic and dynamic software usage. We examine how existing benchmarks evaluate both component-level abilities, such as intent understanding, GUI grounding, navigation, and context tracking, and system-level abilities, such as adaptation, personalization, privacy protection, safety, and computational efficiency. By comparing datasets, environments, and evaluation metrics, the survey reveals clear trends in benchmark design, along with persistent gaps including limited adaptability, vulnerability to malicious interfaces and prompt attacks, lack of interpretability, and significant computational overhead. We highlight emerging directions such as safety aware evaluation, user-centric personalization, lightweight deployment, and zero-shot generalization. This survey aims to serve as a practical guide for researchers who design GUI agents, build benchmarks, or study LLM-driven user interface automation.