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.
