GUI agents powered by Multimodal Large Language Models (MLLMs) are advancing rapidly, yet most existing benchmarks evaluate only task-level success or failure—leaving a fundamental question unanswered: what capabilities do GUI agents most need to succeed in real-world settings? We introduce UniGUI-Bench, a benchmark that jointly verifies task-level outcomes and process-level capability diagnosis. It spans 6 device platforms and 23 application scenarios with bilingual support, comprising over 10,000 evaluation items. Its core design links the two evaluation axes: each task failure can be traced to specific capability deficiencies, enabling fine-grained attribution beyond binary pass/fail. Experiments on 14 frontier models reveal three findings: (1) model rankings diverge substantially from single-platform leaderboards under broader evaluation; (2) different scenarios impose distinct capability bottlenecks identifiable through our attribution framework; (3) targeted training on diagnosed bottleneck dimensions yields consistent improvements on both UniGUI-Bench and OSWorld, providing preliminary evidence toward closing the evaluation–improvement loop. The UniGUI-Bench dataset and evaluation code are available at https://huggingface.co/datasets/AGI-Eval/UniGUI-Bench.

Existing code-generation benchmarks score a single mapping from a complete prompt to a one-shot output. However, real web development is different. Users seldom write a full spec at the start; many requirements only become clear once they look at an intermediate result and react to it. We present Asuka-Bench, a benchmark that pairs underspecified user intent with multi-round refinement, grounded in browser-rendered behavior. Each task is resolved through a closed loop: a Code Agent generates a web project, a UI Agent executes test cases on the deployed site, and a User LLM turns evaluation outcomes into natural-language feedback for the next round. The benchmark comprises 50 web tasks with 784 evaluation criteria and 2402 expected outcomes. We benchmark 8 LLMs across 2 agent frameworks. The results separate models clearly: weighted Task Pass Rate varies by 38 percentage points and models also differ substantially in their ability to repair from feedback. Asuka-Bench is also far from saturated: even the strongest model completes only 52% of projects after three rounds.