GAIA

GAIA asks a deceptively simple question: can an AI system actually finish the kind of task a competent human assistant would, rather than just talk about it? Released in November 2023 by researchers at Meta, Hugging Face and AutoGPT, its 466 questions are each answerable by a person with internet access and ordinary effort, but require an assistant to chain together web browsing, file reading and multi-step reasoning to reach one exact, checkable answer. The benchmark deliberately inverts the usual difficulty curve: instead of expert-level questions that are hard for humans too, it targets tasks that are easy for humans and hard for models.

That gap was stark at launch — GPT-4 with plugins managed only 15% against an average human score of 92%. The rise of dedicated browsing agents closed much of it: Hugging Face’s open-source reproduction of OpenAI’s Deep Research reached 55% on the validation set within about a year, against roughly 67% the post attributed to OpenAI’s own proprietary system, and by late 2025 Princeton’s independently run HAL leaderboard had general-purpose agents built on Claude Sonnet 4.5 clearing 74%.

GAIA’s scores now say as much about the surrounding agent scaffold — how it browses, retries and calls tools — as about the underlying model, which is why serious leaderboards report agent-and-model pairs rather than models alone, and why GAIA’s own community leaderboard is harder to compare against independent runs like HAL’s. It remains one of the standard references for whether an “agent” product is actually completing tasks rather than describing how it would.