LuminaBench
Science benchmark / Vals AI

Terminal-Bench-Science

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 0.1-updated-2026-11-05Verified 6 November 202644 published resultsOfficial source

Terminal-Bench-Science

LuminaBench
Show
20 of 44 models · best variant per model
03507001,0501,4001,220GPT-6 AstraMax1,175GPT-6.1 SolMax1,149Claude Opus 5.5Max1,149Claude Sonnet 5.5Max1,136Gemini 4 ArgonHigh1,116Claude Fable 5.1Max1,104Wonder FullMax1,078Kimi K3.1 ProMax1,071GPT-6 SolMax1,058Claude Opus 5Max1,039WonderMax1,026GPT-5.6 SolMax1,006Claude Fable 5Max994Qwen 3.8 Max987Grok 4.6High981Claude Haiku 5.5Max981Grok 4.7xHigh981Muse Spark 1.3 MaxMax981GPT-5.6 TerraMax974Gemini 3.8 FlashHigh

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 43 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$309001,0001,1001,200Cost per task · USD (log scale)Lumina ScoreGPT-6 AstraGPT-6.1 SolClaude Opus 5.5Claude Sonnet 5.5Gemini 4 ArgonClaude Fable 5.1

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 44 families
10M30M100M9001,0001,1001,200Total tokens per task (log scale)Lumina ScoreGPT-6 AstraGPT-6.1 SolClaude Sonnet 5.5Claude Opus 5.5Gemini 4 ArgonClaude Fable 5.1

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 44 families
Lower resource use and higher score (green); higher resource use and lower score (red). Guides split at the visible selection’s medians.501001502009001,0001,1001,200Time per task · minLumina ScoreGPT-6 AstraKimi K3.1 ProGPT-6.1 SolClaude Opus 5.5Claude Sonnet 5.5Gemini 4 Argon

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

70 scientific terminal tasks with text bash or tmux-screen output. Mini-SWE-agent and Terminus 2 harnesses kept distinct; pass@1, infrastructure errors may be re-attempted, graded failures are not. Not directly comparable with native-agent three-trial official board.

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 − 14.285772727272722) / 16.559320477471893). 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-terminal-bench-science-z75-v1 · experimental
Reference mean
14.285772727272722
Population SD
16.559320477471893
Coverage
Standalone benchmark.
Identity, exclusions & attribution

70 scientific terminal tasks with text bash or tmux-screen output. Mini-SWE-agent and Terminus 2 harnesses kept distinct; pass@1, infrastructure errors may be re-attempted, graded failures are not. Not directly comparable with native-agent three-trial official board.

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.