Hilde · Decision models · answer key v2

Decision model

Liquid d1.1

1 question per request

61.5% of the 2,198 questions right (59.5% to 63.5%, 95% interval). Back to the leaderboard.

How we called it

Kind
Decision model
Access
Liquid AI decisions API
Paid as
Provider service
Model ID
d1.1
Confidence from
Option score
Run
254, 24 October 2026

Settings

Reasoning
off
Request format
1 question per request
Requests at once
1
Temperature
not recorded
Harness seed
0
Harness commit
67908b81827f
Prompt version
254597b8be16

Results

QuestionsAccuracy95% intervalRight
All questions61.5%59.5% to 63.5%1,352 of 2,198
Reading84.6%81.6% to 87.2%555 of 656
Grammar51.7%49.2% to 54.2%797 of 1,542

Comparison group: tuvw. Compare with another setup.

Accuracy by topic
TopicPartAccuracyRight
Word and phrase meaningsReading77.1%101 of 131
Sentence meaningsReading91.6%141 of 154
How a writer explains and develops ideasReading75.7%53 of 70
Topic and main point of a paragraphReading94.9%93 of 98
How a paragraph fits togetherReading75.5%80 of 106
Details that support a paragraph’s main pointReading89.7%87 of 97
Word types, such as nouns and verbsGrammar50.0%70 of 140
Groups of words built around a nounGrammar47.7%31 of 65
Verbs and their formsGrammar58.2%53 of 91
Verb forms used like nouns or modifiersGrammar63.9%46 of 72
Word endings and what they doGrammar51.9%41 of 79
How words are builtGrammar33.3%23 of 69
The roles of words and phrases in a sentenceGrammar57.2%83 of 145
How verbs show who acts and who receives the actionGrammar58.1%43 of 74
Types of sentencesGrammar50.6%43 of 85
Questions combining several grammar topicsGrammar40.9%90 of 220
Sound patterns and changesGrammar32.5%25 of 77
SpellingGrammar50.4%67 of 133
PunctuationGrammar59.8%70 of 117
Problems in sentence wordingGrammar64.0%112 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
Liquid d1.11 question per request56.2%52.7% to 59.7%No image inputNo image input52.3%via Surya OCR 248.7% to 55.8%

Confidence

Answers kept at 99% or more
87, of which 0 wrong (share right: 95.8% to 100.0%, 95% interval); 2,111 skipped, the model less than 99% sure
Calibration gap
2.0 points
AUROC, all questions
0.81 0.79 to 0.82
AUROC, reading
0.88 0.85 to 0.91
AUROC, grammar
0.73 0.71 to 0.76
Brier score
0.172
Accuracy of its surest answers
most confident fifth: 98.6% right; most confident half: 84.2% right; most confident four fifths: 68.7% right
Typical score of a right answer
76.0%
Typical score of a wrong answer
39.8%

Repeats

Run 5 times on all questions.

Answers changed in 5 runs
659 of 2,198
Same wrong answer in all 5
412 of 2,198
Wrong in at least one run
1,071 of 2,198
Accuracy in each run
61.5%, 61.4%, 62.1%, 61.3%, 62.2%

Latency

Latency, median request
0.195 s (this run)
Latency, 90th percentile
0.235 s
Requests at once
1
Throughput
274.9 questions per minute of request time
Request time
8.0 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
$0.007
Whole run
$0.015
Basis
Estimated from published prices and recorded token usage
Price
$0.02 input, output free per 1M tokens (source, 2026-10-24)
Input tokens per question
341
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 request12.9%−0.09 points13%
GPT-6 Astrahigh reasoning · 16 questions per request12.9%−0.09 points13%
GPT-6.1 Solhigh reasoning · 16 questions per request12.9%−0.09 points12%
GPT-6 Astralow reasoning · 16 questions per request12.9%−0.09 points13%
GPT-6 Astralow reasoning · 1 question per request13.3%−0.10 points13%
Gemini 3.8 Flashlow reasoning · 1 question per request12.9%−0.10 points12%
GPT-6.1 Sollow reasoning · 1 question per request15.2%−0.17 points15%
GPT-6.1 Sollow reasoning · 16 questions per request12.9%−0.10 points12%
Claude Opus 5.5high reasoning · 16 questions per request16.8%−0.10 points17%
GPT-6.1 Terrahigh reasoning · 16 questions per request15.7%−0.11 points14%
Claude Opus 5.5low reasoning · 1 question per request16.6%−0.15 points17%
Gemini 3.8 Flashlow reasoning · 16 questions per request13.9%−0.14 points11%
GPT-6.1 Terralow reasoning · 16 questions per request17.0%−0.15 points15%
Claude Opus 5.5low reasoning · 16 questions per request18.7%−0.18 points19%
Claude Sonnet 5.5high reasoning · 1 question per request27.8%−0.22 points28%
Claude Sonnet 5.5high reasoning · 16 questions per request35.5%−0.14 points35%