Performance of GPT-5 Frontier Models in Ophthalmology Question Answering.

Purpose: Novel large language models (LLMs) such as Generative Pretrained Transformer-5 (GPT-5) integrate advanced reasoning capabilities that may enhance performance on complex medical question-answering tasks. For this latest generation of reasoning models, the configurations that maximize both accuracy and cost-efficiency have yet to be established. Our objective was to evaluate the performance and cost-accuracy trade-offs of OpenAI's GPT-5 compared with previous generation LLMs on ophthalmic question answering. Design: Evaluation of diagnostic test or technology. Participants: Generative Pretrained Transformer-5 is a publicly available LLM. Methods: In August 2025, 12 configurations of OpenAI's GPT-5 series (3 model tiers across 4 reasoning effort settings) were evaluated alongside o1-high, o3-high, and GPT-4o, using 260 closed-access multiple-choice questions from the American Academy of Ophthalmology Basic Clinical Science Course data set. The study did not include human participants. Main Outcome Measures: The primary outcome was accuracy on the 260-item ophthalmology multiple-choice question set for each model configuration. The secondary outcomes included head-to-head ranking of configurations using a Bradley-Terry model applied to paired win/loss comparisons of answer accuracy, and evaluation of generated natural language rationales using a reference-anchored, pairwise LLM-as-a-judge framework. Additional analyses assessed the accuracy-cost trade-off by calculating mean per-question cost from token usage and identifying Pareto-efficient configurations. Results: The configuration GPT-5-high achieved the highest accuracy (0.965; 95% confidence interval [CI], 0.942-0.985), significantly outperforming all GPT-5-nano variants ( Conclusions: This study benchmarks the GPT-5 series on a high-quality ophthalmology question-answering data set, demonstrating that GPT-5 with high reasoning effort achieved near-perfect accuracy and outperformed prior reasoning LLMs. This study also introduces an autograder framework for scalable, automated evaluation of LLM-generated answers against reference standards in ophthalmology. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.