DeepSWE vs Effective Cost
X = effective cost (log). Y = DeepSWE %. Color = provider.
DeepSWE vs Effective Cost
DeepSWE vs Effective Cost
X = effective cost (log). Y = DeepSWE %. Color = provider.
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How to read this chart
Each point is a public model. The chart compares DeepSWE % against Effective Cost (per 1M I/O Tokens), with color showing the model provider.
Data sources
More / 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 chartMore / ExperimentalBenchmark Correlation HeatmapA pairwise matrix of source-backed benchmark score correlations across public models. Color shows Pearson r; each cell uses models with both benchmark scores.Data: ARC Prize, Epoch AI FrontierMath, Vals.ai +30 moreOpen chartMore / ExperimentalFlagship Lab Benchmark Correlation HeatmapA pairwise benchmark correlation matrix using only the latest flagship model from each major US and Chinese lab.Data: ARC Prize, Epoch AI FrontierMath, Vals.ai +30 moreOpen chart