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 (P < 0.001), o1-high (P = 0.04), and GPT-4o (P < 0.001), but not o3-high (0.958; 95% CI, 0.931–0.981). The configuration GPT-5-high ranked first in accuracy (1.66x stronger than o3-high) and rationale quality (1.11x stronger than o3-high), as judged by a reference-anchored LLM-as-a-judge autograder. Cost-accuracy analysis identified multiple GPT-5 configurations on the Pareto frontier, with GPT-5-mini-low providing the most optimal low-cost, high-performance configuration. 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 Disclosure(s) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.