Gemini and OpenAI Reach Gold Level at Math Olympiad

Google DeepMind has announced that an advanced version of Gemini with Deep Think scored 35 out of 42 points on the problems from the 2025 International Mathematical Olympiad (IMO). That is a gold-medal score, certified through an evaluation by coordinators from the competition itself.

OpenAI said a few days earlier that an experimental reasoning model had also scored 35 points and fully solved five of the six problems. The two companies have therefore placed their systems at the level of the top human competitors in the world’s toughest pre-university mathematics competition.

Five problems solved like an Olympiad contestant

The IMO brings together high school students from dozens of countries each year to tackle six problems in algebra, geometry, number theory, and combinatorics. Each problem is worth seven points, and contestants have two four-and-a-half-hour sessions to write rigorous proofs.

Gemini Deep Think solved five problems and scored 35 points. The significance lies in more than the number: it presented its answers in natural language, with mathematical proofs that human graders could review. It did not simply find a numerical answer or verify a result that was already known.

Google DeepMind worked with the IMO organization so that the solutions could be evaluated according to the competition’s standards. The system did not officially compete alongside the national delegations, but having Olympiad coordinators grade the work gives the result an unusual degree of external validation for an AI model capability announcement.

OpenAI, for its part, said that three former IMO medalists independently graded its solutions and awarded them 35 points under the competition’s criteria. The company has not publicly identified the model or announced a release date. That caution matters: the result demonstrates a research capability, not a tool that any student or company can use today.

A very rapid leap from 2024

A year ago, Google DeepMind introduced AlphaProof and an upgraded version of AlphaGeometry. Together, they scored 28 out of 42 points on the 2024 IMO problems, a result equivalent to a silver medal.

That system required a far more specialized approach. AlphaProof translated problems into a formal language—a mathematical representation with strict rules that a computer can check—and searched for proofs within that framework. AlphaGeometry focused on geometry. Human intervention was needed to adapt some problem statements to those tools.

Gemini’s result changes the nature of the demonstration. A language model can read the problem statement, explore strategies, and explain the proof in the format a mathematician expects. That potentially makes it more flexible than a system designed for a specific branch of mathematics, although it does not remove the need to verify every step: a proof that appears convincing can still contain an invalid logical leap.

The timing of OpenAI’s announcement also shows how mathematical reasoning has become the new competitive arena for the major AI labs. Mathematics is an especially valuable test because answers can be scored against clear criteria and because the problems demand planning, abstraction, and the ability to sustain a long chain of inferences.

From competition to scientific research

An IMO gold medal does not mean that a system has solved open mathematical problems or that it can replace a researcher. Olympiad problems are new to the participants, but they are designed to have solutions and to be solvable within the exam period using advanced pre-university knowledge.

Even so, the advance has practical implications. In education, these models could help break down a proof, spot an incorrect step, or suggest different paths to a solution. In scientific research, the same ability to explore hypotheses and formalize arguments could speed up highly specific tasks, from algorithm design to the verification of complex calculations.

The next test will be reproducibility. We will need to know how often these models maintain that level beyond six selected problems, how much computational reasoning they require, and how they perform on questions for which no known solution exists. For now, Google and OpenAI have shown that the Olympiad bar—which seemed out of reach for generative models just a year ago—is no longer an exclusively human frontier.