Chess Puzzles vs Effective Cost
X = effective cost (log). Y = Chess Puzzles. Epoch’s 100 novel chess positions, presented as text (FEN). Exact best-next-move accuracy; no vision input. Chess knowledge and planning both contribute.
Chess Puzzles vs Effective Cost
More / ExperimentalSWE-Bench Verified Benchmark ScoresHistorical data. Vals archived cohort; retired from default Software Engineering domain. Each model's SWE-Bench Verified score. Color = provider.Data: Vals.ai, LLM StatsOpen chartMore / ExperimentalTerminal-Bench 2.0 Benchmark ScoresHistorical data. Superseded by 2.1 and 4.0. Legacy Terminal-Bench 2.0 scores retained for historical comparison. Color = provider.Data: Terminal-BenchOpen chartMore / ExperimentalTerminal-Bench 2.1 Benchmark ScoresHistorical data. Superseded in Programmatic Reasoning by Terminal-Bench 4.0. Each 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 2.1, Artificial Analysis Intelligence Index v4.3.1Open chartMore / ExperimentalTerminal-Bench Hard Benchmark ScoresHistorical data. Historical hard subset; not a Terminal-Bench 4.0 result. Each model's Terminal-Bench Hard score. Color = provider.Data: Artificial AnalysisOpen 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 chart