ProofBench Cost Efficiency
Source-backed reasoning and nonreasoning configurations plotted against runtime effective cost. Each line is one canonical model. Color = provider.
ProofBench Cost Efficiency
ProofBench Cost Efficiency
Source-backed reasoning and nonreasoning configurations plotted against runtime effective cost. Each line is one canonical model. Color = provider.
How to read this chart
This chart compares source-backed ProofBench configurations with runtime effective cost. Multiple reasoning levels for the same model are connected; models with one available level remain standalone points. Up and to the left is better.
Data sources
More / ExperimentalProofBench vs Effective CostX = effective cost (log). Y = ProofBench. Color = provider.Data: Artificial Analysis, ARC Prize, Vals.aiOpen chartIQ BenchmarksProofBench Benchmark ScoresEach model's ProofBench score. Color = provider.Data: Vals.aiOpen chartMore / ExperimentalTerminal-Bench 2.0 Benchmark ScoresLegacy Terminal-Bench 2.0 scores retained for historical comparison. Color = provider.Data: Terminal-BenchOpen chartMore / ExperimentalARC-AGI-3 Cost EfficiencyX = 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.Data: ARC PrizeOpen 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 chartMore / ExperimentalARC-AGI-1 Cost EfficiencyX = ARC Prize reported cost/task (log). Y = ARC-AGI-1 %. 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