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.
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
More / ExperimentalARC-AGI-3 vs CostX = ARC Prize reported Cost (V3) (log). Y = ARC-AGI-3 %. Shows all public model generations by default. Color = provider.Data: ARC PrizeOpen chartIQ BenchmarksARC-AGI-3 Benchmark ScoresEach model's ARC-AGI-3 score. Color = provider.Data: ARC PrizeOpen chartCostTask EfficiencyEach dot shows the inverse of the effective-cost usage multiplier. Higher means less price-adjusted task work: 2× is about half the median task effort. Source-backed multipliers are preferred; lineage, peer, and 1× fallbacks are labeled in tooltips.Data: Artificial Analysis, ARC Prize, Vals.aiOpen chartCostIQ vs Effective CostEach model's estimated IQ plotted against effective cost per 1M I/O Tokens (sticker price × measured or imputed usage multiplier).Data: AI IQ methodologyOpen chartMore / ExperimentalTerminal-Bench 2.0 Benchmark ScoresLegacy Terminal-Bench 2.0 scores retained for historical comparison. Color = provider.Data: Terminal-BenchOpen chartMore / ExperimentalARC-AGI-2 Cost EfficiencyX = ARC Prize reported cost/task (log). Y = ARC-AGI-2 %. 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.Data: ARC PrizeOpen chart