ArticleOphthalmology science2026
Comparative Performance of Gemini 3 Pro and GPT-5 Family Models on Ophthalmology Board-Style Questions.
Article in Ophthalmology science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Large Language Models Approximate Inter-Expert Agreement in Glaucoma Suspect and Glaucoma Classification from Multimodal Data.Ophthalmology science · 2026Article
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9 authors.
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Abstract
Objective: To compare the performance of state-of-the-art Gemini and GPT models on ophthalmology board-style questions and examine variation by subspecialty, cognitive complexity, and question type. Design: A cross-sectional evaluation of 12 distinct large language model (LLM) configurations using a standardized ophthalmology question set. Subjects: Five hundred multiple-choice questions (250 from the American Academy of Ophthalmology's Basic and Clinical Science Course [BCSC]; 250 StatPearls). Methods: Twelve configurations of the following LLMs: Gemini 3 Pro, Gemini 2.5 Pro, GPT-5.1 Pro, GPT-5 Pro, GPT-5.2, GPT-5.1, and GPT-5, interpreted the questions using standardized prompting procedures. Questions were categorized by subspecialty, multimodal content (image vs. text-only), and cognitive complexity (first, second, or third order). Accuracy, paired discordance (McNemar tests), and one-way analysis of variance with Tukey correction were used to compare performance. Human benchmarking used BCSC percent-correct data. Main Outcome Measures: Overall accuracy, subspecialty accuracy, image vs. nonimage accuracy, cognitive-complexity accuracy, and paired model-level discordance. Results: Model accuracy ranged from 81.4% to 94.0%. Gemini 3 Pro High Reasoning achieved the highest accuracy (94.0%), followed by Gemini 3 Pro Low Reasoning (92.4%). GPT-5.1 Pro led the GPT family (90.4%), whereas GPT-5.2 Base Model performed lowest (81.4%). Analysis of variance showed significant heterogeneity ( Conclusions: Gemini 3 Pro had the best general-purpose LLM performance on ophthalmology board-style questions, providing near-perfect accuracy, while outperforming all GPT-5 family variants across domains and complexity levels. Significant deficits on image-based and third-order questions highlight persistent multimodal limitations and the need for ongoing benchmarking using challenging, clinically grounded datasets. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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