LuminaBench
Math benchmark / Vals AI

ProofBench v1.1

Lumina VerifiedLumina checked the published sources, versions, model labels, score extraction and conversion. External evaluations were run by their publishers; Lumina has not independently rerun or reproduced them.

Accuracy

Version 1.1-updated-2026-10-29Verified 6 November 202654 published resultsOfficial source

ProofBench v1.1

LuminaBench
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20 of 54 models · best variant per model
03006009001,2001,103Claude Fable 5.1Max1,103Claude Opus 5.5Max1,103Claude Sonnet 5.5Max1,103logicalintelligence/alephprover1,101Claude Opus 5Max1,101Gemini 4 ArgonHigh1,101GPT-6 AstraMax1,101GPT-6.1 SolMax1,091Claude Fable 5Max1,089Wonder FullMax1,082Kimi K3.1 ProMax1,077Claude Haiku 5.5Max1,073Kimi K3Max1,070aristotle/aristotle1,066WonderMax1,063GPT-5.6 SolMax1,063GPT-6 SolMax1,049Claude Sonnet 5Max1,044Hy4 Preview1,042GPT-5.6 TerraMax

Lumina Score vs Cost per Task

Published per-test USD and Duration/Test retain their native definitions. Tokens use published averages or original aggregate counts divided by token_tasks_measured; total = input + output, with cache and reasoning subsets retained separately rather than counted twice. No task values are inferred from token prices or compute budgets.

LuminaBench
15 of 52 families
Lower resource use and higher score (green); higher resource use and lower score (red). Guides split at the visible selection’s medians.$0.3$1$3$108009001,0001,100Cost per task · USD (log scale)Lumina ScoreClaude Haiku 5.5Claude Sonnet 5.5Claude Fable 5.1Claude Opus 5.5logicalintelligence/alephproverGPT-6.1 Sol

Lumina Score vs Tokens

Published per-test USD and Duration/Test retain their native definitions. Tokens use published averages or original aggregate counts divided by token_tasks_measured; total = input + output, with cache and reasoning subsets retained separately rather than counted twice. No task values are inferred from token prices or compute budgets.

LuminaBench
15 of 52 families
300K1M8009001,0001,100Total tokens per task (log scale)Lumina ScoreClaude Opus 5.5Claude Fable 5.1Claude Sonnet 5.5Claude Opus 5Gemini 4 ArgonGPT-6 Astra

Lumina Score vs Time per Task

Published per-test USD and Duration/Test retain their native definitions. Tokens use published averages or original aggregate counts divided by token_tasks_measured; total = input + output, with cache and reasoning subsets retained separately rather than counted twice. No task values are inferred from token prices or compute budgets.

LuminaBench
15 of 53 families
Lower resource use and higher score (green); higher resource use and lower score (red). Guides split at the visible selection’s medians.10308009001,0001,100Time per task · min (log scale)Lumina ScoreGPT-6.1 SolClaude Sonnet 5.5Claude Fable 5.1Claude Opus 5.5logicalintelligence/alephproverClaude Opus 5

Published per-test USD and Duration/Test retain their native definitions. Tokens use published averages or original aggregate counts divided by token_tasks_measured; total = input + output, with cache and reasoning subsets retained separately rather than counted twice. No task values are inferred from token prices or compute budgets. Missing metrics are omitted from their graph; quality scores remain available.

Results table

Method & coverage

ProofBench v1.1 private test split, Lean 4 formal proofs with Mathlib search and Lean execution; no informal proof supplied. General models up to 40 turns, no internet. Specialized proof systems retain their own harness, toolchain and web access distinctions.

Published per-test USD and Duration/Test retain their native definitions. Tokens use published averages or original aggregate counts divided by token_tasks_measured; total = input + output, with cache and reasoning subsets retained separately rather than counted twice. No task values are inferred from token prices or compute budgets.

How the Lumina conversion works

1,000 + 75 × ((original score − 55.98148148148148) / 32.081776724570794). The result is rounded once. Scores may tie.

Frozen initial observed text-only cohort; one mean per exact publisher release/API, balancing repeated settings/routes within that release. Original score and protocol remain separate. No inferred variants or future refitting.

Calibration
vals-proofbench-z75-v1 · experimental
Reference mean
55.98148148148148
Population SD
32.081776724570794
Coverage
Standalone benchmark.
Identity, exclusions & attribution

ProofBench v1.1 private test split, Lean 4 formal proofs with Mathlib search and Lean execution; no informal proof supplied. General models up to 40 turns, no internet. Specialized proof systems retain their own harness, toolchain and web access distinctions.

Publisher standard errors retained; no confidence level or interval inferred. Aggregate reporting scope is recorded in the current source-specific publication review.

Standalone text benchmark; no new combined index. Source identity/effort/protocol qualifications remain in Data and CSV.

All published reasoning settings, including Claude Max, are included by default. Display labels use Low and xHigh; source labels remain in the retained records.

Official source · Publication terms / evidence

Publisher terms. Scores and resource figures are attributed to the original publisher.