Terminal-Bench 2.1 Cost Efficiency
Source-matched reasoning-effort score and task-cost configurations. Each line is one canonical model. Color = provider.
Terminal-Bench 2.1 Cost Efficiency
Terminal-Bench 2.1 Cost Efficiency
Source-matched reasoning-effort score and task-cost configurations. Each line is one canonical model. Color = provider.
How to read this chart
Each line uses the shared model-configuration dataset to compare source-matched reasoning-effort levels on Terminal-Bench 2.1. 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 / ExperimentalTerminal-Bench 2.1 vs Effective CostX = effective cost (log). Y = Terminal-Bench 2.1 %. Color = provider.Data: Artificial Analysis, ARC Prize, Vals.ai +2 moreOpen chartIQ BenchmarksTerminal-Bench 2.1 Benchmark ScoresEach model's Terminal-Bench 2.1 pass@1 score, using Artificial Analysis as canonical and Vals.ai as fallback. Color = provider.Data: Artificial Analysis Terminal-Bench v2.1, Vals.ai Terminal-Bench 2.1Open 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