Anthropic models lead completeness but trail OpenAI on medical benchmarks
Verbose medical answers earn completeness points but introduce more claims that can contradict the record, so length itself becomes an accuracy liability. Teams building clinical documentation or decision-support tools should weight length-adjusted scores over raw ones, since the top model changes depending on that choice. Against alternatives like GPT-6 Astra, Claude models suit use cases where covering every rubric point matters more than brevity.
Artificial Analysis compared 14 models on both MLCR-AA, its leaderboard for Wisedocs' Medical Long Context Reasoning benchmark, and OpenAI's HealthBench Professional. Anthropic's Claude Sonnet 5.5, Opus 5.5 and Fable 5.1 hold the top three MLCR-AA completeness scores, roughly 19-23 points ahead of the best OpenAI model, and Claude Opus 5.5 and Sonnet 5.5 post the highest HealthBench Professional raw scores at 71.4% and 70.8%, ahead of GPT-6 Astra at 69.7%. OpenAI models lead on accuracy, scoring 89-97% on MLCR-AA accuracy against 19-61% for Anthropic models, which write about 4-6 times more per answer. On combined measures, GPT-6 Astra (max) ties Claude Sonnet 5.5 for first on MLCR-AA and narrowly leads the HealthBench Professional length-adjusted score, while Claude Opus 5.5 drops from first on raw score to sixth.
On MLCR-AA and HealthBench Professional, Anthropic models give the most complete answers but OpenAI models are more accurate and concise
We compared 14 models with results on both MLCR-AA, our leaderboard for @wisedocsai's Medical Long Context Reasoning benchmark, and @OpenAI's HealthBench Professional. MLCR-AA grades answers about long medical records on completeness (covering every key point of the expert's answer) and accuracy (no claim contradicting the record), and its score requires both. HealthBench Professional grades replies to clinicians against physician-written rubrics: the raw score is the share of rubric points earned, while the length-adjusted score penalizes longer answers.
Key takeaways:
➤ Anthropic models lead on completeness and raw score: Claude Sonnet 5.5, Opus 5.5 and Fable 5.1 (all max with fallback) from @AnthropicAI hold the top three MLCR-AA completeness scores, ~19-23 points ahead of the best OpenAI model, and Opus and Sonnet post the highest HealthBench Professional raw scores (71.4% and 70.8%), narrowly ahead of GPT-6 Astra (69.7%)
➤ OpenAI models lead on accuracy and conciseness: OpenAI models hold the top MLCR-AA accuracy scores. Longer answers are less likely to be judged accurate, as each extra claim is another chance to contradict the record: Anthropic models write ~4-6x more per answer than OpenAI models, and score 19-61% on accuracy against 89-97%
➤ The gap closes on scores that reward both: GPT-6 Astra (max) ties Claude Sonnet 5.5 for #1 on MLCR-AA and narrowly leads the HealthBench Professional length-adjusted score, while Claude Opus 5.5 falls from #1 on the raw score to #6 length-adjusted
