PA Bench: Evaluating Frontier Models on Multi-Tab Pa Tasks (vibrantlabs.com)

Vibrant Labs has introduced PA Bench, a benchmark designed to assess the capabilities of advanced computer-use agents in executing complex, long-horizon personal assistant workflows that span multiple applications. Unlike traditional benchmarks that evaluate isolated tasks—like adding an item to a cart or creating a calendar event—PA Bench reflects real-world interactions where agents must seamlessly switch between applications, reason about distributed information, and coordinate actions to achieve intended outcomes. This new benchmark aims to provide comprehensive evaluations under controlled, deterministic conditions, significantly enhancing the understanding of how well these agents mimic human-like assistance. The introduction of PA Bench is significant for the AI/ML community as it offers a structured method to scrutinize the performance of frontier models such as Claude Opus 4.6 and Gemini across multi-tab environments. Early evaluations showed varying success rates among different models; for instance, Claude Opus 4.6 achieved a task success rate of 68.8%, demonstrating its ability to adeptly navigate user interactions and adapt when errors occur. On the other hand, models like Gemini 3 Pro displayed planning proficiency but frequently struggled with execution accuracy. This emerging benchmark not only identifies strengths and weaknesses among models but also emphasizes the necessity of developing agents capable of tackling realistic workflows, thereby pushing the boundaries of AI assistant functionalities.