Today, we’re releasing “Early science acceleration experiments with GPT‑5⁠(opens in a new window),” a paper co-authored with collaborators at universities and national laboratories including Vanderbilt, UC Berkeley, Columbia, Oxford, Cambridge, Lawrence Livermore National Laboratory, and The Jackson Laboratory. It compiles early case studies across math, physics, biology, computer science, astronomy, and materials science in which GPT‑5 helped researchers synthesize known results in a novel way, conduct powerful literature review, accelerate tough computations, and even generate novel proofs of unsolved propositions. The paper also documents limitations. Our goal is to give the community a clear view of what these systems can and cannot do today in research settings.

These case studies show how, in the hands of experts, GPT‑5 is accelerating scientific discovery, and why that acceleration matters:

The mission of OpenAI for Science is to accelerate scientific discovery: to help researchers explore more ideas, test hypotheses faster, and uncover insights that would otherwise take significant time. We do this by pairing frontier models with the right tools, workflows, and collaborations.

We work closely with researchers across academia, industry, and national labs. These collaborations help us understand where the models are useful, where they fail, and how to integrate them into the scientific process—from literature review and proof generation to modeling, simulation, and experimental design.