Over the last year, our models have reached major milestones, including achieving gold-medal performance at the International Math Olympiad and the International Olympiad in Informatics. In parallel, we’re starting to see our most capable models, such as GPT‑5, meaningfully accelerate real scientific workflows. Researchers are using these systems for tasks such as literature search across disciplines and languages and working through complex mathematical proofs. In many cases, the model shortens work that might have taken days or weeks to hours. This progress is documented in our paper Early science acceleration experiments with GPT‑5⁠, released in November 2025, which presents early evidence that GPT‑5 can measurably accelerate scientific workflows.

As accelerating scientific progress is one of the most promising opportunities for AI to benefit humanity, we’re improving our models on difficult math and science tasks and working on the tools that will help scientists get the most from them.

When GPQA⁠(opens in a new window), a “Google-Proof” science benchmark of questions written by PhD experts, was released in November 2023, GPT‑4 scored 39%, below the expert baseline of 70%. Two years later, GPT‑5.2 scored 92%. As models’ reasoning and knowledge capabilities continue to scale, more difficult benchmarks will be important to measure and forecast models’ ability to accelerate scientific research. Prior scientific benchmarks largely focus on multiple-choice questions, are saturated, or are not centrally focused on science.

To bridge this gap, we’re introducing FrontierScience: a new benchmark built to measure expert-level scientific capabilities. FrontierScience is written and verified by experts across physics, chemistry, and biology, and consists of hundreds of questions designed to be difficult, original, and meaningful. FrontierScience includes two tracks of questions: Olympiad, which measures Olympiad-style scientific reasoning capabilities, and Research, which measures real-world scientific research abilities. Providing more insight into models’ scientific capabilities helps us track progress and advance AI-accelerated science.