AI IQ

ARC-AGI-3

A bird's-eye view of the benchmark: what it measures and every AI IQ chart built on it.

About ARC-AGI-3

ARC-AGI-3 measures abstract reasoning in interactive game-like environments. An agent must explore, learn from feedback, and adapt its strategy to complete each level. Scores report Relative Human Action Efficiency, while Cost (V3) records the evaluation cost published by the ARC Prize leaderboard.

ARC-AGI-3 Cost Efficiency
ARC-AGI-3 Cost Efficiency
X = ARC Prize reported Cost (V3) (log). Y = ARC-AGI-3 %. Each line connects one model's published reasoning-effort levels, so the score-vs-cost tradeoff is visible per model. Defaults to the current model generation. Color = provider.
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How to read this chart

Each line is one model run at several reasoning-effort levels on ARC-AGI-3. Every point shows the score and Cost (V3) at that effort, with cheaper runs on the left. Up and to the left is better; a line that stays flat while moving right is buying little extra score with the extra spend. The current generation is shown by default; use the Generation filter to include previous and older models.

Data sources
ARC Prize
ARC-AGI-3 Benchmark Scores
ARC-AGI-3 Benchmark Scores
Each model's ARC-AGI-3 score. Color = provider.
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How to read this chart

Bars rank models by the source-backed benchmark value used for this chart. Longer bars indicate higher published scores.

Data sources
ARC Prize
ARC-AGI-3 vs Cost
ARC-AGI-3 vs Cost
X = ARC Prize reported Cost (V3) (log). Y = ARC-AGI-3 %. Shows all public model generations by default. Color = provider.
Controls:
ARC-AGI-31:1Cost

How to read this chart

Each point is a public model. The chart compares ARC-AGI-3 % against Cost (V3), with color showing the model provider.

Data sources
ARC Prize

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