Ship Sense RUN 2026-07-10 · BANK 6cb4779d6b7c
67 REAL PRIVATE ITEMS; 5 SYNTHETIC EXAMPLES EXCLUDED
Product judgment benchmark

Product judgment,
under uncertainty

How 17 frontier models score when the right move is to stop: refuse a feature, name what the data can't support, or hold a call under pressure. The answer keys are one operator's real product decisions, not invented for a benchmark.

Ranked #1 · 67/67 items
Muse Spark 1.1
89.9
7 models in the leader-overlap band · naive floor 39.1. Ship Sense Score, 0–100.

What we measure

"Product taste" is hard to score. These three parts are observable, and each maps to a documented model weakness.

Dimension 01

RestraintR

What do you refuse to build, and where do you draw an AI agent's autonomy line?

Graded: SHIP / DEFER / KILL per feature against a documented key; traps weighted 2×; some items add a hard capacity cap.

targets over-eagerness
Dimension 02

HonestyH

What can this data — and the model's own confident output — actually support?

Graded: Binary checks for documented landmines and enumerated false claims, including overconfident conclusions and over-skeptical dismissal.

targets confident fabrication
Dimension 03

ConvictionC

Do you hold a defensible call under pressure, and update only on real evidence?

Graded: Multi-turn: resist social pressure and weak, p-hacked, or confident-but-wrong output, while updating on genuine new evidence.

targets sycophancy

The Ship Sense Score (0–100) is the equal-weight mean of the three dimension scores, so a dimension with more items can't dominate, reported with a 95% bootstrap CI.

Leaderboard Run 2026-07-10

Anthropic OpenAI Google xAI Meta
556065707580859095Muse Spark 1.1 — 89.9 [86.5, 92.8] · R 0.85 · H 0.85 · C 1.00Muse Spark 1.1*89.9Grok 4.5 — 87.4 [84.0, 90.7] · R 0.83 · H 0.82 · C 0.97Grok 4.5*87.4GPT-5.5 — 87.0 [83.2, 90.5] · R 0.86 · H 0.81 · C 0.94GPT-5.5*87.0Claude Fable 5 — 86.6 [82.7, 90.1] · R 0.86 · H 0.82 · C 0.92Claude Fable 5*86.6GPT-5.6 Sol — 86.4 [83.1, 89.6] · R 0.88 · H 0.77 · C 0.94GPT-5.6 Sol*86.4GPT-5.6 Terra — 84.4 [80.3, 88.1] · R 0.84 · H 0.79 · C 0.91GPT-5.6 Terra*84.4Claude Sonnet 4.6 — 82.9 [78.6, 87.0] · R 0.79 · H 0.84 · C 0.86Claude Sonnet 4.6*82.9GPT-5.4 mini — 82.5 [79.1, 85.8] · R 0.77 · H 0.81 · C 0.90GPT-5.4 mini82.5Claude Opus 4.8 — 81.8 [76.8, 86.8] · R 0.83 · H 0.83 · C 0.80Claude Opus 4.881.8Gemini 3.1 Pro — 81.2 [76.6, 85.2] · R 0.82 · H 0.70 · C 0.92Gemini 3.1 Pro81.2GPT-5.6 Luna — 81.0 [77.1, 84.8] · R 0.83 · H 0.80 · C 0.80GPT-5.6 Luna81.0Grok 4.3 — 80.1 [76.0, 84.0] · R 0.73 · H 0.73 · C 0.94Grok 4.380.1Gemini 3.5 Flash — 79.1 [75.0, 83.1] · R 0.80 · H 0.71 · C 0.86Gemini 3.5 Flash79.1Claude Haiku 4.5 — 79.0 [75.2, 82.6] · R 0.74 · H 0.78 · C 0.84Claude Haiku 4.579.0Claude Sonnet 5 — 77.7 [72.2, 83.0] · R 0.76 · H 0.84 · C 0.72Claude Sonnet 577.7Gemini 3.1 Flash-Lite — 72.5 [67.9, 77.0] · R 0.76 · H 0.64 · C 0.78Gemini 3.1 Flash-Lite72.5GPT-5.4 nano — 63.1 [58.2, 68.7] · R 0.64 · H 0.84 · C 0.41GPT-5.4 nano63.1

Dot = point score · whisker = 95% item-clustered bootstrap CI · * = leader-overlap band · the naive “ship everything, flag nothing, always cave” baseline scores 39.1 — below this scale.

#ModelReleased$/M in/out Ship Sense Score (95% CI) RestraintHonestyConviction
1*Muse Spark 1.1Meta2026-07-09$1.25/$4.2589.995% CI 86.5–92.80.850.851.00
2*Grok 4.5xAI2026-07-08$2/$687.495% CI 84.0–90.70.830.820.97
3*GPT-5.5OpenAI2026-04-23$5/$3087.095% CI 83.2–90.50.860.810.94
4*Claude Fable 5Anthropic2026-06-09$10/$5086.695% CI 82.7–90.10.860.820.92
5*GPT-5.6 SolOpenAI2026-07-09$5/$3086.495% CI 83.1–89.60.880.770.94
6*GPT-5.6 TerraOpenAI2026-07-09$2.5/$1584.495% CI 80.3–88.10.840.790.91
7*Claude Sonnet 4.6Anthropic2026-02-17$3/$1582.995% CI 78.6–87.00.790.840.86
8GPT-5.4 miniOpenAI2026-03-17$0.75/$4.582.595% CI 79.1–85.80.770.810.90
9Claude Opus 4.8Anthropic2026-05-28$5/$2581.895% CI 76.8–86.80.830.830.80
10Gemini 3.1 ProGoogle2026-02-19$2/$1281.295% CI 76.6–85.20.820.700.92
11GPT-5.6 LunaOpenAI2026-07-09$1/$681.095% CI 77.1–84.80.830.800.80
12Grok 4.3xAI$1.25/$2.580.195% CI 76.0–84.00.730.730.94
13Gemini 3.5 FlashGoogle2026-05-19$1.5/$979.195% CI 75.0–83.10.800.710.86
14Claude Haiku 4.5Anthropic2025-10-01$1/$579.095% CI 75.2–82.60.740.780.84
15Claude Sonnet 5Anthropic2026-06-30$3/$1577.795% CI 72.2–83.00.760.840.72
16Gemini 3.1 Flash-LiteGoogle$0.25/$1.572.595% CI 67.9–77.00.760.640.78
17GPT-5.4 nanoOpenAI$0.2/$1.2563.195% CI 58.2–68.70.640.840.41
Naive baselinegameability floor · not ranked39.1
Choosing a model?If this judgment score is the deciding criterion, list price can break a close call. Muse Spark 1.1 is the least expensive model in the leader-overlap band at $1.25/$4.25 per 1M tokens. Claude Fable 5 is the most expensive at $10/$50. Capability fit, latency, privacy, and provider terms still matter.

Bars show the point estimate (marker) and 95% bootstrap CI (band), clustered by item. * marks the descriptive leader-overlap band: that model's interval overlaps the point leader's interval. This is not a test of pairwise equality. Per-dimension cells are weighted correctness (0–1); $/M is list price in USD per 1M input/output tokens.

Head-to-head 136 paired comparisons

Point scores rank; paired tests separate. Each cell replays the same items for both models and asks whether the difference survives a sign-flip test with Holm correction across the whole family. Of 136 comparisons, 40 are decisive; the best single record is 9 decisive wins. Every other pair on this board is statistically inseparable — a finding, not a failure.

1234567891011121314151617wins
Muse Spark 1.1···9
Grok 4.5·····5
GPT-5.5······5
Claude Fable 5······3
GPT-5.6 Sol·····4
GPT-5.6 Terra··········2
Claude Sonnet 4.6··········2
GPT-5.4 mini·······2
Claude Opus 4.8·········2
Gemini 3.1 Pro·········1
GPT-5.6 Luna·········1
Grok 4.3·········1
Gemini 3.5 Flash·······1
Claude Haiku 4.5······1
Claude Sonnet 5········1
Gemini 3.1 Flash-Lite·0
GPT-5.4 nano0

Reading a row: that model against each column opponent (columns ordered by rank). ▲ = a decisive win, called by the Holm-corrected sign-flip test at p ≤ 0.05 across all 136 comparisons. △ = the unadjusted 95% paired interval excludes zero but the family-wise verdict is inconclusive — suggestive, not a win. · = no separation. ▼ / ▽ mirror the losses. The wins column counts decisive wins only (16 possible).

How to read it · limits

  • Single-author keys, automated cross-check. Keys are one operator's real on-the-job decisions. In place of a second human rater, a frontier-model jury flags any key it reads as overstrict or ambiguous, and keys are anchored to real outcomes where they exist (src/judge_audit.py). Automated and self-improving, but the jury can share biases with the keys, so rankings are directional.
  • No formal power study yet. ~13 points is a conservative cross-model resolution guide inferred from observed marginal intervals, not a minimum detectable effect. Paired comparisons can resolve smaller differences because item difficulty cancels; their intervals and multiplicity correction govern.
  • Grading is deterministic whole-word alias matching, not a semantic judge. It can miss a correctly-phrased-but-unusual flag. The false-alarm check is negation-aware (warning against a claim doesn't count as asserting it); punctuation-edge aliases need textual alternatives. Rubrics + examples are published so the grading is auditable.
  • Cautious-answer gameability is not fully closed. Honesty rewards documented landmines and avoids enumerated false conclusions, but it does not penalize every invented caveat. The naive baseline tests over-eagerness, not a flag-everything strategy.
  • Generation uncertainty is conditional. Two generations are averaged, while the item bootstrap treats that observed pair as fixed. Intervals generalize over case sampling, not every stochastic response the same model could produce.

Run history

A score only compares to others on the same bank definition; the version label marks every bank or scoring change, so a jump between versions reads as "the eval changed," not "the models changed." Keeping the case bank private reduces direct exposure and gaming; it does not prove that providers have never seen similar work. Any item that leaks signal retires.

VersionRunModelsBankBank fingerprintWhat changed
v3.02026-07-101767 real private items; 5 synthetic examples excluded (R24 H24 C19)6cb4779d6b7c67 items; career-span additions 2016-2025 — GM-era portfolio, launch, pricing, and founder-pressure decisions from five companies
v2.02026-07-071750 real private items; 5 synthetic examples excluded (R18 H18 C14)fa054e29e93d50 items; bank recomposed to client-and-own-product work only (work-sample items retired); spec-scoping, pricing, and exec-communication coverage added.
v1.32026-07-011142 real private items; 5 synthetic examples excluded (R15 H15 C12)491f08725a7a42 items; model-limit and growth-loop honesty batch. Re-graded 2026-07-07 after a wrong-key correction (third self-audit).
v1.22026-06-301036 real private items; 5 synthetic examples excluded (R13 H12 C11)692b622fb25336 items; strict-hold conviction scoring (hedging to CONDITIONAL no longer passes hold turns).
v1.12026-06-091131 real private items; 5 synthetic examples excluded (R11 H10 C10)004735cb1beb31 items; Claude Fable 5 scored on its launch day. Unreadable responses became coverage gaps, never zeros (second self-audit).
v1.02026-05-311029 scored items (R9 H9 C11)5f9ab56ba81cFirst official board: 29 real items, 10 models. Honesty grading made polarity-aware after the first self-audit.