MMMU-Pro Cost Efficiency
X = 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.
MMMU-Pro Cost Efficiency
MMMU-Pro Cost Efficiency
X = 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.
How to read this chart
Each line uses the shared model-configuration dataset to compare source-matched reasoning-effort levels on MMMU-Pro. Every point shows the score and AA cost per task for that exact configuration. Canonical score bars and bell curves remain one point per model.
More / ExperimentalMMMU-Pro vs Effective CostX = effective cost (log). Y = MMMU-Pro score. Color = provider.Data: Artificial Analysis, ARC Prize, Vals.ai +1 moreOpen chartIQ BenchmarksMMMU-Pro Benchmark ScoresEach model's MMMU-Pro score. Color = provider.Data: Artificial Analysis, Artificial Analysis model leaderboardOpen 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