BrowseComp explained: what it measures and how search APIs compare

BrowseComp is a 1,266-question benchmark released by OpenAI in April 2025 to evaluate AI agents’ ability to find short, verifiable facts that require persistent multi-step web research, unlike simpler single-fact benchmarks such as SimpleQA. Its questions are constructed by combining several broad constraints into a uniquely identifying answer, and they were designed to resist quick searches and early browsing models; OpenAI’s original results ranged from 0.6% accuracy for GPT-4o to 51.5% for Deep Research, while human trainers solved fewer than one-third of attempted questions after roughly two hours. The benchmark uses exact-answer accuracy and confidence calibration, with findings suggesting that more test-time browsing, repeated attempts, and answer selection methods can improve performance, although web access can also increase overconfidence in incorrect responses. Later vendor and independent evaluations report substantially higher scores for modern search-assisted and deep-research systems, but these comparisons use different subsets, agents, dates, judges, and cost structures, making small score differences unreliable. The text also notes risks including test-data leakage, limited coverage of broader research tasks, and possible saturation among high-end systems, while describing BrowseComp-Plus as a reproducible fixed-corpus variant and BrowseComp-ZH as a Chinese-web version.

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