Gut Feel Benchmark

  • Research
  • First run
  • 17 October 2026

Summary

Seven contestants answered 100 items each: three decision models and three chat models, one of them run a second time with high reasoning. OpenAI Luna led with a Gut Score of 59.0 at a median 0.30 seconds per answer, and Jev came second at 51.3. Claude Opus 5.5 judged best, with a skill of 71.2, but took 2.0 seconds per answer and placed fourth at 32.0.

Decision models answered faster. The best chat models answered better. Gut Score counts an answer only when it beats the deadline, so the three decision models took the top three places.

Leaderboard

Gut Score averages skill over seven deadlines from 0.25 to 16 seconds, with every late answer counted as the base rate. Skill is the share of the base rate’s squared error that a forecast removes, with no deadline. Both run to 100, and both average the poker, base-rate and Hacker News packs.

Gut Score and 95% intervalSkill, no deadline
  1. OpenAI Luna
  2. Jev
  3. Microsoft D-1
  4. Opus 5.5
  5. Opus 5.5 high
  6. GPT-6.1 Sol
  7. Gemini 3.8 Flash
−20020406080
ContestantGut Score95% intervalSkillMedian latency
OpenAI LunaDecision model59.056.1 to 62.565.10.30 s
JevDecision model51.345.0 to 56.257.20.26 s
Microsoft-Decision-1Decision model37.526.4 to 47.146.80.30 s
Claude Opus 5.5Chat, reasoning off32.025.0 to 37.371.22.0 s
Claude Opus 5.5Chat, high reasoning25.718.8 to 29.469.93.1 s
GPT-6.1 SolChat, reasoning off12.4−11.4 to 16.466.95.7 s
Gemini 3.8 FlashChat, reasoning off0.3−24.5 to 7.928.812.3 s

Twenty-five items per pack leave wide intervals. Jev beats Microsoft-Decision-1 in 99% of bootstrap resamples, but Microsoft-Decision-1 and Opus with reasoning off are not separated, at 81%, and neither are Sol and Gemini, at 90%.

The clock

The decision models keep nearly all their skill from half a second on, while the chat models keep none of it before two seconds. OpenAI Luna scores 24.3 at the quarter-second deadline and 63.3 at half a second, against a skill of 65.1. Opus with reasoning off scores nothing before 2 seconds, 58.0 at 4 seconds and its full 71.2 only at 16.

Each cell below is skill at one deadline, shaded more strongly the more skill it holds.

Contestant0.25 s0.5 s1 s2 s4 s8 s16 sNo limit
OpenAI Luna24.363.365.165.165.165.165.165.1
Jev17.255.957.257.257.257.257.257.2
Microsoft D-10.028.646.846.846.846.846.846.8
Opus 5.50.00.0−0.126.258.068.971.271.2
Opus 5.5 high0.00.00.07.244.158.669.969.9
GPT-6.1 Sol0.00.00.00.015.035.036.966.9
Gemini 3.8 Flash−7.2−7.2−7.2−7.2−7.211.426.228.8

Reasoning

High reasoning bought Opus no skill. It used 141 reasoning tokens per answer instead of 36, scored 69.9 against 71.2 and slowed from 2.0 to 3.1 seconds, which cost it 6 points of Gut Score.

Reasoning off held for none of the chat models. Sol used 162 reasoning tokens per answer and Gemini 3.8 Flash 2,169, with no warning from the SDK, and Gemini ran past the 120-second limit on 23 of 25 poker hands, so a quarter of its items count as failures.

ContestantReasoning tokens per answerSkillMedian latencyAnswered
Claude Opus 5.5Chat, reasoning off3671.22.0 s100 of 100
Claude Opus 5.5Chat, high reasoning14169.93.1 s100 of 100
GPT-6.1 SolChat, reasoning off16266.95.7 s100 of 100
Gemini 3.8 FlashChat, reasoning off2,16928.812.3 s75 of 100

By pack

The exact packs separate the field and the outcome packs barely do. Every chat model and OpenAI Luna scored 100.0 on base rates, and on poker the chat models that answered scored 98.6 to 99.7, against 95.8 for Luna and 80.2 for Jev. On Hacker News no contestant passed 14.7.

Heart disease sits outside the headline, in grey, because its records have been public since 1988 and a model may remember them. Luna scored 6.4 there, while Jev scored 43.0 and Opus 68.5 to 73.1.

ContestantPokerBase ratesHacker NewsHeart disease
OpenAI Luna95.8100.0−0.66.4
Jev80.283.77.843.0
Microsoft D-169.263.28.054.9
Opus 5.598.8100.014.768.5
Opus 5.5 high98.6100.011.073.1
GPT-6.1 Sol99.7100.01.068.8
Gemini 3.8 Flash7.7100.0−21.579.3

Cost

The run used $3.51 of AI Gateway credit. A thousand answers from Jev or Luna cost about 2 cents, and a thousand from Opus with high reasoning cost $5.02.

Recorded token counts explain $1.66 of the bill. The rest is most likely Gemini’s 25 timed-out calls, which Gateway appears to bill although they return nothing.

ContestantInput tokens per itemOutput tokens per itemCost per 1,000 answers
Microsoft-Decision-1Decision model1061$0.004
JevDecision model37120$0.016
OpenAI LunaDecision model2320$0.023
GPT-6.1 SolChat, reasoning off210179$2.21
Claude Opus 5.5Chat, reasoning off49047$2.91
Claude Opus 5.5Chat, high reasoning490153$5.02
Gemini 3.8 FlashChat, reasoning off1881,636$8.37

Method

Each contestant answered the first 25 items of four packs. Poker hands are scored against exact equity over all 1,712,304 boards, and screening-test problems against the exact posterior by Bayes’ rule. Cleveland Clinic patients are scored against their angiograms, and Hacker News stories posted from 2 to 9 October 2026 against whether they reached 100 points.

Every item went through AI SDK 7’s experimental_decide on Vercel AI Gateway, natively for the decision models and through a structured-output adapter for the chat models. Calls ran four at a time with a 120-second limit and no retries, after one untimed warm-up call per contestant. Gateway answered a request that does no model work in 40 to 56 milliseconds, so network time is a small share of every latency.

No deadline scores above a contestant’s skill with no deadline, so a late answer can lower a score but never raise it. The intervals come from 1,000 paired bootstrap resamples of the items, each scored against the pack’s own base rate.

Limits

  • Twenty-five items per pack leave the middle of the table unsettled.
  • GPT-6.1 Sol and Gemini 3.8 Flash with high reasoning have not run.
  • The intervals cover which items were drawn, not run-to-run variation in a model’s answers or latency.
  • All latencies come from one machine on one evening, so they compare with each other and not with other runs.
  • No human expert has answered yet, although a person can join a run from the terminal.

Files

Code and method GitHub

The repository holds the code and the four packs, and its README describes the scoring and how to run the benchmark.

Full report report.md

It holds every table, including log loss, calibration error and accuracy for each pack.

Every answer predictions.jsonl

Each line records one answer with its probability, latency, tokens and any error.