Comparative performance of nine large language models on professional Parkinson’s disease tasks: a multidimensional benchmark study

Background Parkinson’s disease (PD) requires both disease-specific knowledge and contextual clinical reasoning. However, most evaluations of medical large language models (LLMs) emphasize closed-ended questions and factual accuracy, providing limited insight into broader dimensions of performance on professional PD tasks. Methods Nine contemporary LLMs were evaluated using a 200-item English-language professional PD benchmark spanning five clinical domains, four difficulty levels, and multiple question formats. Responses were assessed for expert-rated factual accuracy, clinical reasoning, Global Quality Scale (GQS), and Flesch–Kincaid Grade Level (FKGL). Results The nine models generated 1,800 responses. Between-model separation was minimal for expert-rated factual accuracy (Kendall’ s W = 0.031) but greater for FKGL (W = 0.235), Clinical Reasoning (W = 0.256), and GQS (W = 0.301). Between-model separation in expert-rated factual accuracy was small for closed-ended questions (W = 0.020, p = 0.001) but greater for open-ended questions (W = 0.121, p < 0.001). Expert-rated factual accuracy remained weakly discriminative across PD domains and difficulty levels, while GQS showed the greatest domain-specific separation in Safety & Ethics (W = 0.431). Flagship variants consistently showed higher Clinical Reasoning and GQS, but not uniformly higher factual accuracy. Conclusion Contemporary LLMs showed limited between-model separation in expert-rated factual accuracy within this benchmark and scoring framework, whereas greater differentiation emerged in open-ended tasks, clinical reasoning, overall response quality, and readability. Multidimensional evaluation may therefore provide a more informative characterization of model performance and help inform task-specific model selection and oversight in medical applications.