LLM Reference
activeMedicalSafety

HealthBench Hard

Metric: Length-adjusted score (higher is better)Introduced: 2025

Hard subset of OpenAI HealthBench for more difficult medical conversations. LLMReference uses the length-adjusted score for cross-model comparison. High benchmark score alone doesn't make a model the right pick — weigh it against pricing, API availability, and release date.

Models ranked

3

tracked on this benchmark

Score band

33.1 – 32.0

best → lowest tracked

Snapshot trend

need ≥2 snapshots

Leaderboard

Tracked models ranked by Length-adjusted score (higher is better).

Compare candidates
#Model variant and provenanceScore
1
GPT-5.6 Sol
Version: length-adjusted; raw 31.1; mean response 1751 charsHarness: Not recordedEvaluator: Not recordedObserved: Jun 26, 2026Confidence: Not recordedSource

Notes: Official GPT-5.6 preview system-card row. Raw score is recorded only in the version label so rankings use the comparable length-adjusted metric.

33.1
2
GPT-5.6 Terra
Version: length-adjusted; raw 34.3; mean response 2199 charsHarness: Not recordedEvaluator: Not recordedObserved: Jun 26, 2026Confidence: Not recordedSource

Notes: Official GPT-5.6 preview system-card row. Raw score is recorded only in the version label so rankings use the comparable length-adjusted metric.

32.7
3
GPT-5.6 Luna
Version: length-adjusted; raw 31.4; mean response 1923 charsHarness: Not recordedEvaluator: Not recordedObserved: Jun 26, 2026Confidence: Not recordedSource

Notes: Official GPT-5.6 preview system-card row. Raw score is recorded only in the version label so rankings use the comparable length-adjusted metric.

32.0

How to read this benchmark

This benchmark scores models where higher is better. Scores are useful for directional filtering and shortlisting — not for universal quality ranking. Prefer benchmarks closest to your workload, then validate the linked model pages for pricing, context window, and provider availability.

Trust this score when

  • There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
  • The model list covers the same version family you can actually deploy today.
  • Top candidates overlap with your required routing and feature requirements.

Be cautious when

  • There is only one benchmark snapshot or the dataset appears stale.
  • The benchmark metric direction is opposite of your decision objective.
  • The score difference between options is narrow and likely within implementation variance.

FAQ

What does the HealthBench Hard benchmark measure?

Hard subset of OpenAI HealthBench for more difficult medical conversations. LLMReference uses the length-adjusted score for cross-model comparison. On this page it lists 3 tracked model variants where higher is better.

Is a higher HealthBench Hard score always better?

For this benchmark, higher is better. A high score helps you shortlist, but confirm pricing, context window, and provider availability on each model page before committing — the top scorer is not always the right pick for your workload or budget.

How current is this HealthBench Hard data?

This benchmark was last reviewed on Jun 26, 2026. Re-check the linked model pages for the freshest provider and pricing detail.

Related benchmarks

Last reviewed: Jun 26, 2026