Evaluating Deep Research performance in the wild with the DRACO benchmark

Perplexity has released DRACO, an open-source benchmark for evaluating deep research agents on authentic, production-derived research tasks rather than narrow synthetic questions. Built from millions of user requests and refined through privacy filtering, contextualization, difficulty screening, and expert review, DRACO contains 100 tasks across ten domains, each assessed with detailed rubrics covering factual accuracy, analytical breadth and depth, presentation, and primary-source citation. The benchmark uses weighted binary criteria, including penalties for hallucinations and unsupported claims, and employs LLM judges whose relative system rankings were validated across multiple models. In Perplexity’s evaluation of four research systems with browser and code-execution tools, its Deep Research product achieved the highest pass rates in every domain, particularly Law and Academic research, led in accuracy, analysis, and citation quality, and recorded the lowest latency. Perplexity says DRACO, its rubrics, judge prompt, and dataset are publicly available, while future versions are intended to support more languages, multi-turn interactions, and additional domains.

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