ARC Prize - Leaderboard

Understanding the Leaderboard

ARC-AGI has evolved from its first versions (ARC-AGI-1 and 2) which measured passive fluid intelligence, to ARC-AGI-3 which challenges AI agents to adapt on the fly to novel interactive environments.

The scatter plot above visualizes the critical relationship between cost-per-task and performance - a key measure of efficiency. True intelligence isn't just about solving problems, but solving them efficiently with minimal resources.

Interpreting the data

• Reasoning Systems Trend Line solutions display connected points representing the same model at different reasoning levels. These trend lines illustrate how increased reasoning time affects performance, typically showing asymptotic behavior as thinking time increases.
• Base LLMs solutions represent single-shot inference from modern general-purpose language models on ARC-AGI-1 and ARC-AGI-2, without extended reasoning capabilities. ARC-AGI-3 results use either the Standard harness (carries forward model-selected notes throughout the environment) or the Provider Adapter harness (preserves reasoning state and compacts longer conversations so the model can reuse prior work).
• Kaggle Systems solutions showcase competition-grade submissions from the ARC Prize Kaggle challenges, running under strict competition-specific compute constraints. These represent purpose-built, efficient methods specifically designed for the ARC Prize.

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