On the Navier–Stokes Millennium Prize Problem | OpenAISkip to main contentResearchProductsBusinessDevelopersCompanyFoundation(opens in a new window)Log inTry ChatGPT(opens in a new window)ResearchProductsBusinessDevelopersCompanyFoundation(opens in a new window)Try ChatGPT(opens in a new window)LoginOpenAISeptember 8, 2026ResearchPublicationOn the Navier–Stokes Millennium Prize ProblemRead the paperLink to Lean formalized proofLoading…ShareThe problemThe problemThe resultHow we found the proofConcurrent workProgress and responsibilityThe problemThe resultHow we found the proofConcurrent workProgress and responsibilityWe’re sharing a solution to the Navier–Stokes existence and smoothness problem, one of the Millennium Prize Problems. This proof, produced by an internal OpenAI system, shows that the dynamics of the Navier-Stokes equations for fluid motion can develop a singularity in finite time. We’re sharing both a writeup of the proof and a formalization in Lean.The Millennium Prize Problems⁠(opens in a new window) represent some of the deepest questions at the frontier of mathematics. The question of whether smooth three-dimensional fluid motion can break down has remained unresolved for roughly 90 years.A major goal of our work is to empower scientists to advance research and technology that benefits all of humanity. To solve the Navier–Stokes problem, we used an internal model that is significantly more capable than GPT‑6 Astra. We believe it is important to inform the world about the pace of AI progress and what to expect from upcoming models.The problemThe Navier–Stokes equations use Newton’s second law of motion (“F=ma”) to describe how fluids move. Importantly, they treat a fluid as a continuous medium rather than tracking individual molecules. These equations are used for aircraft design, weather forecasting, and the study of blood flow.A fundamental open question for these dynamical equations has been whether the continuum approximation of the fluid can break down. Specifically, can the Navier–Stokes equations for a three-dimensional incompressible fluid with constant density develop a “singularity,” even when the motion starts smoothly? Here, a singularity means the dynamics lead to speeds in the fluid growing without bound within a finite amount of time. The development of a singularity would have to happen despite the presence of viscosity, which tends to smooth out motion. Because a real fluid cannot move infinitely fast, this would mark a breakdown in how the equations model the fluid. To continue modeling the system, one would then need to track the behaviour of each particle individually.The equations date to the nineteenth-century work of Claude-Louis Navier and George Gabriel Stokes. In 1934, Jean Leray proved that solutions exist in a generalized sense, but whether they always remain smooth became a central unanswered question. In 2000, the Clay Mathematics Institute named the Navier–Stokes existence and smoothness problem one of seven Millennium Prize Problems.The resultOur system produced an analytical proof and a Lean formalization that an initially smooth fluid at rest can develop a singularity in a finite time. The fluid has a smooth force applied to it, and its energy remains finite through the entire dynamics, from rest to the formation of the singularity. This resolves the Navier–Stokes Millennium Prize problem by establishing statement “C” (and also “D”) in the official Millennium Prize formulation⁠(opens in a new window).The solution is a vortex, a spinning swirl of fluid, that spirals inward and gets increasingly elongated, like spaghetti. This central region shrinks while it speeds up in such a way that its energy still stays finite, as required by the laws of physics. The technical challenge is for the equations to develop the breakdown through the motion of the fluid itself, rather than, for example, us putting in an infinite force by hand. More mathematically, the terms in the Navier–Stokes equations that describe the motion—acceleration, pressure gradients, momentum transfer, viscosity—must both become big yet cancel in a precise way. This detailed balance leaves a smooth external force even as the velocity of the fluid grows without bound.A snapshot of local incompressible motion. Orange marks faster angular rotation; teal marks slower rotation. Circulating speed also depends on radius. The trajectories show inward spiraling and axial stretching.How we found the proofSince August 28 we have been training a new internal model that has exhibited unprecedented performance in our benchmarks, including mathematics. This model’s training is ongoing and its performance continues to improve.On Tuesday, September 1, we heard rumors that two Millennium Prize problems had been resolved. Inspired by these rumors and by the step change in performance of our internal model, we launched an effort to evaluate it on all open Millennium Prize problems and a few other high-impact problems.We used a system of coordinating agents powered by our internal model. The agents had access to tools such as the ability to read from a cached version of the internet and the ability to run code. Agents were subdivided into groups with the ability to communicate within the group. The groups varied in size, and the group that produced the Navier–Stokes resolution involved on the order of 10,000 concurrent agents. At all times we maintained the same strict safeguards that we apply to all our frontier model evaluations, including monitoring and isolation.For each problem, we prompted different groups of agents with different variants of the problem statement, covering all variants of the problem. For the Navier–Stokes problem, we suggested versions “A” and “B” (particular forms of the Navier–Stokes problem which would result in a proof) and versions “C” and “D” (which would result in a disproof) to separate groups of agents.In addition to the full Millennium Prize problems, we asked our multiagent system to try a set of “easier” problems. One of these problems was a similar blowup question for the limit of the Navier–Stokes problem with the viscosity term removed. This is known as the regularity problem for the Euler equations, and our agents surprised us by resolving this question. The specific variant of the question that they resolved was the unforced version, where no external force is applied to the fluid. Nearly 100 agents worked together for approximately 50 hours to produce our Euler regularity disproof.1Once we saw the Euler solution, we thought that Navier–Stokes was the most promising problem to work on. Thus, we decided to devote our resources to Navier–Stokes. To do so, we shifted agents away from the other Millennium Problems and prompted these agents with the Euler resolution. When a further trained version of our internal model became available over the course of the effort, we updated our agents to that model.We encouraged different groups of agents to explore a diversity of approaches. After some time, we cross-pollinated the agent groups by using Codex to consolidate the most useful insights from each agent group. These follow-up prompts drew on the agents’ own intermediate results. The group that found the solution to Navier–Stokes was guided in such a way.The agents arrived at their resolution on Saturday, September 5, about 88 hours after the first agents were launched. Lean formalization and verification took an additional 17 hours via GPT‑6 Astra.Across all attempted problems, the agents sent 4.9 million messages and used about 300 billion output tokens. In the process of resolving the Navier–Stokes problem, the agents sent 2.7 million messages and used approximately 130 billion output tokens.Concurrent workOur effort began on September 1st after hearing a rumor which we later realized was related to Levent Alpöge, an Anthropic employee, and Tristan Buckmaster, a math professor at NYU. After the completion of our full project and Lean verification (on September 6th), believing from the rumor they also had a solution of Navier–Stokes, we reached out to them to offer a concurrent release of our result and to recognize their priority in a joint announcement. At that point we found out that they had a resolution of the forced Euler problem. In these discussions we offered them visibility into all of the prompts we used and later to see the proof. We recognize the priority of their work on forced Euler and congratulate them on their remarkable mathematical achievement.We (the researchers and the agents) did not see any of their work through any means until they released it publicly — in particular, no specific user data was accessed in order to solve this problem. While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models⁠. However, our proofs differ significantly and even the precise results proved are different in the Euler case (forced vs unforced).Progress and responsibilityOur goal in releasing this result is to report on the substantial progress of our AI models. We do not intend to claim the Millennium Prize for this result.This milestone represents substantial work by mathematicians and AI researchers. However, this is not a culmination, but rather a snapshot in time, of progress on AI development.We believe we are now in the next period of AI progress⁠, and today’s results provide further evidence of this. We are focusing on understanding this model, and using what we learn to help us guide and pace how we pursue further advances in capability. One of our key goals⁠ is to build AI systems which are steerable, accountable, and connected to people, which may require more deliberate choices about the pace of progress, as we continue our mission to ensure AGI benefits all of humanity.2026Generative ModelsAuthorOpenAIFootnotes1Read the Euler proof paper⁠(opens in a new window) · Link to Lean formalized proof⁠(opens in a new window)ResearchResearch IndexResearch OverviewEconomic ResearchLatest AdvancementsGPT-6GPT-5.6GPT-5.5GPT-5.4SafetySafety ApproachDeployment Safety(opens in a new window)Security & PrivacyTrust & TransparencyProductsChatGPT(opens in a new window)ChatGPT Business(opens in a new window)ChatGPT Enterprise(opens in a new window)ChatGPT for Education(opens in a new window)CodexRelease NotesAPI PlatformOverviewAPI Log In(opens in a new window)Docs(opens in a new window)BusinessOverviewSolutionsResourcesCustomer StoriesPartner NetworkContact SalesDevelopersApps SDK(opens in a new window)Open ModelsDocs(opens in a new window)Resources(opens in a new window)Developer Forum(opens in a new window)CompanyAbout UsOur CharterCareersNewsSupportHelp Center(opens in a new window)MoreStoriesAcademySupply Co.LivestreamsPodcastRSSTerms & PoliciesTerms of UsePrivacy PolicyOther Policies (opens in a new window)(opens in a new window)(opens in a new window)(opens in a new window)(opens in a new window)(opens in a new window)(opens in a new window)OpenAI © 2015–2026Your privacy choicesEnglishUnited States

The effort detailed in the provided text concerns the solution to the Navier–Stokes existence and smoothness problem, one of the Millennium Prize Problems. This mathematical inquiry centers on whether three-dimensional fluid motion governed by the Navier–Stokes equations can develop a singularity in finite time, despite the presence of viscosity, which typically acts to smooth out motion. The Navier–Stokes equations are fundamental to describing fluid dynamics, utilizing Newton’s second law to model how fluids move as continuous media. A breakdown in this continuum approximation would imply that the fluid's speeds could grow without bound in a finite period, signaling a failure in the conventional mathematical modeling of the system.

The problem itself stems from the nineteenth-century work of Claude-Louis Navier and George Gabriel Stokes. While Jean Leray had previously established the existence of generalized solutions in 1934, the question of whether these solutions always remain smooth remained an open challenge until the Clay Mathematics Institute designated it a Millennium Prize Problem in 2000.

The research team utilized an internal system, which was described as being more capable than GPT-6 Astra, to produce an analytical proof and a formalization in Lean demonstrating that an initially smooth fluid at rest can indeed develop a singularity in finite time under smooth external forces while maintaining finite energy across the dynamic evolution. The result describes the singularity as an inward-spiraling vortex where the central region contracts while its speed increases, yet its total energy remains bounded, consistent with physical laws. This necessitates a precise mathematical balance where the terms describing acceleration, pressure gradients, momentum transfer, and viscosity become large yet precisely cancel each other out, preserving a smooth external force even as the fluid velocity appears to grow without bound locally.

The proof was achieved through a complex multiagent approach. The process began by training a new internal model over several weeks, and subsequently, a system of coordinating agents powered by this model was deployed with access to tools such as internet caching and code execution capabilities. These agents were organized into specialized groups that communicated, allowing them to tackle the problem using varied strategies. For the Navier–Stokes challenge, different groups were prompted with various forms of the problem statement, including variants that would result in a proof, variants that would result in a disproof, and related regularity problems for the simpler Euler equations. This multiagent system resolved the required proofs and formalizations through iterative exploration, cross-pollination of insights using Codex, and focused guidance toward the solution.

Concurrent work was also conducted with other researchers, including Levent Alpöge and Tristan Buckmaster of NYU, who had independently achieved a resolution for the related forced Euler problem. While this concurrent achievement involved different mathematical contexts—the Navier–Stokes case versus the Euler case—it underscored the potential interconnectedness of these foundational problems.

The release of this result is framed not as an attempt to claim the Millennium Prize, but rather as a reflection of substantial progress in artificial intelligence and mathematics research. The team emphasizes that this milestone represents a snapshot of AI advancement, providing evidence for the next phase of progress. The focus moving forward is intended to guide the pace of future capability development by prioritizing the building of steerable and accountable AI systems that are connected to people, ensuring that advancements benefit all of humanity.