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structured output benchmark

Internal API instruction following (hard)

Internal API instruction following (hard) benchmark - specific documentation not found in official sources

Updated Aug 11, 2026

Models7
Model coverage7
MetricScore
EvidenceB

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Internal API instruction following (hard) Ranking

Higher score ranks better on this benchmark.

7 rows
Columns

Show columns

01OPGPT-5OpenAI64.0%100.0%7CAug 11, 2026
02OPGPT-4.5OpenAI54.0%83.3%7CAug 11, 2026
03OPo3-miniOpenAI50.0%66.7%7CAug 11, 2026
04OPGPT-4.1OpenAI49.1%50.0%7CAug 11, 2026
05OPGPT-4.1 miniOpenAI45.1%33.3%7CAug 11, 2026
06OPGPT-4.1 nanoOpenAI31.6%16.7%7CAug 11, 2026
07OPGPT-4oOpenAI29.2%0.0%7CAug 11, 2026

Internal API instruction following (hard) Score Distribution

A closer view of the leading scores on this benchmark.

Internal API instruction following (hard)

Internal API instruction following (hard) Highlights

The leading models and scores on this benchmark.

Rank #1GPT-564.0%Rank #2GPT-4.554.0%Rank #3o3-mini50.0%Rank #4GPT-4.149.1%

What is Internal API instruction following (hard)?

What Internal API instruction following (hard) measures and how its scores work.

Internal API instruction following (hard) benchmark - specific documentation not found in official sources

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
Internal API instruction following (hard)
Modality
text
Primary category
structured output
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
internal-api-instruction-following-(hard)|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about Internal API instruction following (hard).

Which model scores highest on Internal API instruction following (hard)?

GPT-5 is currently ranked first with 64.0%.

What does Internal API instruction following (hard) measure?

Internal API instruction following (hard) benchmark - specific documentation not found in official sources

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

7 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.