LuminaBench
Coding benchmark / Vals AI

ProgramBench

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-updated-2026-11-05Verified 6 November 202665 published resultsOfficial source

ProgramBench

LuminaBench
Show
20 of 64 models · best variant per model
04008001,2001,6001,498Claude Opus 5.5Max1,169Claude Fable 5.1Max1,155Claude Sonnet 5.5Max1,126GPT-6 AstraMax1,055Claude Opus 5Max1,055GPT-6.1 SolMax1,040Claude Haiku 5.5Max1,040Gemini 4 ArgonHigh1,040Muse Spark 1.3 MaxMax1,026Claude Fable 5Max1,026Kimi K31,026GPT-6 SolMax1,012GPT-5.6 SolMax1,012GLM 5.3Max998Claude Opus 4.8Max998Gemini 3.8 FlashHigh998Grok 4.6High983Claude Sonnet 4.6Max983DeepSeek V4.1 FlashMax983Grok 4.7xHigh

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 51 families
Lower resource use and higher score (green); higher resource use and lower score (red). Guides split at the visible selection’s medians.$3$10$301,0001,2001,400Cost per task · USD (log scale)Lumina ScoreClaude Opus 5.5GPT-6.1 SolClaude Opus 5GPT-6 AstraClaude Fable 5.1Claude Sonnet 5.5

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 27 families
10M30M100M1,0001,2001,400Total tokens per task (log scale)Lumina ScoreClaude Opus 5.5GPT-6 AstraGemini 4 ArgonMuse Spark 1.3 MaxClaude Sonnet 5.5GPT-6.1 Sol

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 64 families
Lower resource use and higher score (green); higher resource use and lower score (red). Guides split at the visible selection’s medians.301003001,0001,2001,400Time per task · min (log scale)Lumina ScoreClaude Opus 5.5GPT-6 AstraClaude Opus 5GPT-6.1 SolClaude Sonnet 5.5Claude Fable 5.1

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

200 compiled CLI programs and behavioral text specifications; mini-SWE-agent bash-only cleanroom reproduction, offline sandbox, no decompilers or instrumentation; 1,000 steps, six-hour limit.

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 − 1.0846153846153845) / 2.623223546544961). 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-programbench-z75-v1 · experimental
Reference mean
1.0846153846153845
Population SD
2.623223546544961
Coverage
Standalone benchmark.
Identity, exclusions & attribution

200 compiled CLI programs and behavioral text specifications; mini-SWE-agent bash-only cleanroom reproduction, offline sandbox, no decompilers or instrumentation; 1,000 steps, six-hour limit.

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.