LuminaBench
Beta benchmark / Vals AI

Agent Poker Bench

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.

TrueSkill rating

Version 1-updated-2025-12-23Verified 6 November 202617 published resultsOfficial source

Agent Poker Bench

LuminaBench
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17 of 17 models · best variant per model
03006009001,2001,118GPT-5.21,081GPT-51,077Gemini 3 Flash (12/25)1,064DeepSeek V3.21,050Grok 4.1 fast1,049Gemini 3 Pro (11/25)1,023DeepSeek-V3.11,019Claude Sonnet 4.5997GPT-5.1992Grok 4 Fast990Claude Opus 4.5989Gemini 2.5 Pro967GPT-Oss 120b962Kimi K2 Thinking940Qwen 3 Max Preview877GLM-4.6805Llama 4 Maverick

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 17 families
Lower resource use and higher score (green); higher resource use and lower score (red). Guides split at the visible selection’s medians.$0.01$0.03$0.18009001,0001,100Cost per task · USD (log scale)Lumina ScoreGPT-5.2Grok 4 FastGPT-5Gemini 3 Flash (12/25)Grok 4.1 fastDeepSeek V3.2

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 17 families
Lower resource use and higher score (green); higher resource use and lower score (red). Guides split at the visible selection’s medians.0.10.3138009001,0001,100Time per task · min (log scale)Lumina ScoreGPT-5.2DeepSeek-V3.1GPT-5Gemini 3 Flash (12/25)DeepSeek V3.2Grok 4.1 fast

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

Text fixed-structure no-limit Texas Hold’em cash games, ten players, 100 hands, 100 big blinds, stack carry-over. TrueSkill rating, not financial return or independent 100-hand percent accuracy.

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 − 1040.8226615076037) / 57.92476078513323). 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-poker-agent-z75-v1 · experimental
Reference mean
1040.8226615076037
Population SD
57.92476078513323
Coverage
Standalone benchmark.
Identity, exclusions & attribution

Text fixed-structure no-limit Texas Hold’em cash games, ten players, 100 hands, 100 big blinds, stack carry-over. TrueSkill rating, not financial return or independent 100-hand percent accuracy.

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.