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We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

We want to celebrate with people using GPT-6. We did this for GPT-5.5 and it was really fun. We’re getting together in SF on September 16 to talk about the model, what we should build next, and mostly just to hang out. Apply by Sep 10: https://t.co/3LIMfGOBRQ

OpenAI's unreleased internal model sits on a much higher capability curve than GPT-6 Astra. the one that just solved the 90-year old Navier-Stokes Millennium Prize math Problem. the new model also appears to use additional reasoning compute much more productively. That's how throwing 10,000 agents and enormous compute at Navier-Stokes could produce something qualitatively different from simply running Astra longer.

MASSIVE: OpenAI's 10,000 AI agents, powered by a model significantly more capable than GPT-6 Astra, solved a math problem unresolved for roughly 90 years. Looks like, in this new world, the scaling law may be agent count, not model size: enough capable agents can search far more scientific ideas than any human group ever could. when tens of thousands of strong agents can explore, criticize, and combine ideas, many problems that are "too hard" today may become mostly a compute problem. For this one, for 90 years, mathematicians could not answer whether smooth 3D fluid flow can suddenly break down. OpenAI's proposed proof says it can. To find this solution, OpenAI split thousands of agents into groups exploring different ideas, then shared the best findings between them. The search took 88 hours and used about 130B output tokens across 2.7M messages. GPT-6 Astra then spent another 17 hours checking and converting the proof into Lean, a system that verifies mathematical proofs. That Lean version is public, so mathematicians can inspect the argument themselves.

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This model represents a step-function improvement on many benchmarks, and its training is ongoing. Our internal model group arrived at the Navier–Stokes solution in 88 hours, using around 10,000 coordinating AI agents. Throughout the effort, we maintained the strict safeguards—including monitoring and isolation—that we apply to all our frontier evaluations.