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Announcing ARC-AGI-3 The only unsaturated agentic intelligence benchmark in the world Humans score 100%, AI <1% This human-AI gap demonstrates we do not yet have AGI Most benchmarks test what models already know, ARC-AGI-3 tests how they learn
We build benchmarks that reveal the gap between what's easy for humans, hard for AI ARC-AGI has repeatedly identified inflection points in AI progress, including the emergence of reasoning systems and the rise of capable AI agents. ARC-AGI-3 is the next step in that journey
We created an in-house game studio and built 135 novel environments from scratch No instructions, Core Knowledge Priors-only In order to beat these games, AI must: • Explore the environment • Form hypotheses • Execute a plan • Learn and adapt
ARC-AGI-3 is a useful research tool to analyze model behavior Key failure modes seen in our early testing: • Thinking it is playing another game • Holding on to early hypothesis • Unable to forecast into the future Both AI + human runs have sharable replays Watch Gemini 3.1 do well on some games, poorly on others: <a target="_blank" href="https://arcprize.org/replay/34a9614e-9b77-4f82-bda4-ff1320f2e15b" color="blue">arcprize.org/replay/34a9614…</a> <a target="_blank" href="https://arcprize.org/replay/d0e07686-493f-4801-b154-043c8158a967" color="blue">arcprize.org/replay/d0e0768…</a>
ARC-AGI-3 gives us a formal measure to compare human and AI skill acquisition efficiency Humans don’t brute force - they build mental models, test ideas, and refine quickly How close AI is to that? (Spoiler: not close)
Also live today: ARC Prize 2026 - 3 tracks, $2,000,000 in prizes available! Get involved: • Play a Game: <a target="_blank" href="https://arcprize.org/tasks/ls20" color="blue">arcprize.org/tasks/ls20</a> • Build Agents: <a target="_blank" href="https://docs.arcprize.org" color="blue">docs.arcprize.org</a> • Win Prizes: <a target="_blank" href="https://arcprize.org/competitions/2026" color="blue">arcprize.org/competitions/2…</a>
