SciCode Cost Efficiency
Source-matched reasoning-effort score and task-cost configurations. Each line is one canonical model. Color = provider.
SciCode Cost Efficiency
SciCode 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 SciCode. 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.
Data sources
More / ExperimentalSciCode vs Effective CostX = effective cost (log). Y = SciCode %. Color = provider.Data: Artificial Analysis, ARC Prize, Vals.aiOpen chartIQ BenchmarksSciCode Benchmark ScoresEach model's SciCode score. Color = provider.Data: Artificial AnalysisOpen 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