Hilde · Decision models · answer key v2

Decision model

GPT-6 Luna

Decisions API, beta · 1 question per request

63.6% of the 2,198 questions right (61.6% to 65.6%, 95% interval). Back to the leaderboard.

How we called it

Kind
Decision model
Access
OpenAI Decisions API (public beta)
Paid as
Provider service
Model ID
gpt-6-luna
Variant
Decisions API, beta
Confidence from
Option score
Run
143, 8 October 2026

Settings

Reasoning
off
Request format
1 question per request
Requests at once
4
Temperature
not recorded
Harness seed
0
Harness commit
not recorded
Prompt version
not recorded

Results

QuestionsAccuracy95% intervalRight
All questions63.6%61.6% to 65.6%1,398 of 2,198
Reading85.5%82.6% to 88.0%561 of 656
Grammar54.3%51.8% to 56.8%837 of 1,542

Comparison group: rstuv. Compare with another setup.

4 questions got no valid answer; they count as wrong.

Accuracy by topic
TopicPartAccuracyRight
Word and phrase meaningsReading87.0%114 of 131
Sentence meaningsReading88.3%136 of 154
How a writer explains and develops ideasReading72.9%51 of 70
Topic and main point of a paragraphReading94.9%93 of 98
How a paragraph fits togetherReading74.5%79 of 106
Details that support a paragraph’s main pointReading90.7%88 of 97
Word types, such as nouns and verbsGrammar50.0%70 of 140
Groups of words built around a nounGrammar41.5%27 of 65
Verbs and their formsGrammar65.9%60 of 91
Verb forms used like nouns or modifiersGrammar62.5%45 of 72
Word endings and what they doGrammar48.1%38 of 79
How words are builtGrammar44.9%31 of 69
The roles of words and phrases in a sentenceGrammar51.0%74 of 145
How verbs show who acts and who receives the actionGrammar64.9%48 of 74
Types of sentencesGrammar57.6%49 of 85
Questions combining several grammar topicsGrammar36.4%80 of 220
Sound patterns and changesGrammar42.9%33 of 77
SpellingGrammar75.9%101 of 133
PunctuationGrammar53.0%62 of 117
Problems in sentence wordingGrammar68.0%119 of 175

Page images (preliminary)

Preliminary: these numbers are not final and may change. This setup is condition A of the Page images tab, which asks the 763 questions with underlined or numbered words as text (A), as a page image (B), as both (C) and through OCR models (D); the share answered right, with its 95% interval.

ModelA · text onlyaccuracy, higher is betterB · image onlyaccuracy, higher is betterC · text plus imageaccuracy, higher is betterBest OCR routeaccuracy, higher is better
GPT-6 Luna (Decisions API)Decisions API, beta · 1 question per request55.8%52.3% to 59.3%32.6%29.4% to 36.0%56.6%53.1% to 60.1%50.3%via Surya OCR 246.8% to 53.9%

Confidence

Answers kept at 99% or more
368, of which 3 wrong (share right: 97.6% to 99.7%, 95% interval); 1,830 skipped: 1,826 with the model less than 99% sure and 4 without a valid answer
Calibration gap
2.4 points
AUROC, all questions
0.82 0.80 to 0.84
AUROC, reading
0.86 0.82 to 0.89
AUROC, grammar
0.77 0.74 to 0.79
Brier score
0.166
Accuracy of its surest answers
most confident fifth: 98.6% right; most confident half: 86.6% right; most confident four fifths: 72.8% right
Typical score of a right answer
83.0%
Typical score of a wrong answer
39.5%

Repeats

We did not repeat this setup.

Latency

Latency, median request
0.191 s (this run)
Latency, 90th percentile
0.250 s
Requests at once
4
Throughput
1214.8 questions per minute of request time
Request time
1.8 minutes, over the requests whose answers were kept

Latency: the time from sending one request to receiving its answer, measured from our computer, network and any retries within the request included. Request time: the time at least one request was in flight, over the requests whose answers were kept. It is not the run's elapsed time: it leaves out requests that failed and any pause before a run was resumed.

Cost

$ per 1,000 questions
$0.040
Whole run
$0.089
Basis
Estimated from published prices and recorded token usage
Price
$0.10 input, output free per 1M tokens (source, 2026-10-08)
Input tokens per question
404
Output tokens per question
0

Going first in front of an LLM

Replay of recorded answers, cutoff chosen on half the questions, averaged over the splits. Savings are estimates from API prices; the Method page explains how.

LLM behind itShare takenAccuracy changeSaving
Gemini 3.8 Flashhigh reasoning · 16 questions per request15.5%−0.08 points14%
GPT-6 Astrahigh reasoning · 16 questions per request8.2%−0.09 points8%
GPT-6.1 Solhigh reasoning · 16 questions per request12.0%−0.12 points10%
GPT-6 Astralow reasoning · 16 questions per request8.2%−0.09 points7%
GPT-6 Astralow reasoning · 1 question per request8.1%−0.09 points8%
Gemini 3.8 Flashlow reasoning · 1 question per request15.5%−0.08 points11%
GPT-6.1 Sollow reasoning · 1 question per request8.1%−0.09 points7%
GPT-6.1 Sollow reasoning · 16 questions per request8.2%−0.09 points5%
Claude Opus 5.5high reasoning · 16 questions per request19.2%−0.07 points19%
GPT-6.1 Terrahigh reasoning · 16 questions per request17.8%−0.06 points10%
Claude Opus 5.5low reasoning · 1 question per request21.6%−0.12 points21%
Gemini 3.8 Flashlow reasoning · 16 questions per request16.7%−0.12 points2%
GPT-6.1 Terralow reasoning · 16 questions per request20.4%−0.07 points11%
Claude Opus 5.5low reasoning · 16 questions per request20.9%−0.10 points20%
Claude Sonnet 5.5high reasoning · 1 question per request26.0%−0.14 points25%
Claude Sonnet 5.5high reasoning · 16 questions per request34.4%−0.15 points33%