IQ vs EQ vs Cost in 3D
3D scatter: X = EQ, Y = IQ, Z = cost per 1K queries (log). Color = provider. Drag to rotate.
IQ vs EQ vs Cost in 3D
IQ vs EQ vs Cost in 3D
3D scatter: X = EQ, Y = IQ, Z = cost per 1K queries (log). Color = provider. Drag to rotate.
How to read this chart
The 3D scatter combines quality and cost dimensions in one view. Drag the plot to rotate and compare clusters.
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 / ExperimentalMMLU-Pro Cost EfficiencyMMLU-Pro score versus runtime effective cost. Explicit reasoning and nonreasoning variants are connected; other published configurations remain standalone points. Color = provider.Data: Artificial Analysis, ARC Prize, Vals.ai +2 moreOpen chartMore / ExperimentalMMMU-Pro Cost EfficiencyX = Artificial Analysis reported cost per task (log). Y = MMMU-Pro score. Each line connects one model's published reasoning-effort levels from the shared configuration dataset; models with one available level remain standalone points. Color = provider.Data: Artificial Analysis, Artificial Analysis model leaderboardOpen chart