# OpenAI says its model solved 90 of math's 500 hardest open problems > Source: > Published: 2026-10-07 14:44:48+00:00 *OpenAI dropped 722 new manuscripts on GitHub this week claiming progress on 90 of roughly 500 of the field's toughest unsolved problems. One mathematician called it history. Others want to see the work first.* On October 6, OpenAI published 722 manuscripts, organized into 372 problem families, all generated by an unreleased internal model and posted to a public GitHub repository. The company says the batch covers 90 of around 500 problems drawn from some of mathematics' hardest open-problem lists, built from an evaluation run across roughly 4,000 candidate research questions. According to coverage from Latent Space's AINews newsletter, one mathematician in the thread reacting to the release called it potentially "the most significant moment" in more than a hundred years of mathematical history. That is a much bigger number than what OpenAI put out last time. StartupFortune covered the prior batch of 722 proofs when mathematicians were still digesting it and mostly unimpressed. This release layers a specific, bigger claim on top: not just a pile of manuscripts, but 90 solved items against a named denominator of 500. The shift from "we made progress on problems" to "we cleared 90 of the field's hardest 500" is the headline here, and it's the number readers are now trying to check. The compute OpenAI says it used is notable on its own. Each result in the batch ran, on average, about three hours of ChatGPT Pro-level thinking time. That's not a supercomputer cluster grinding for weeks. It's closer to the kind of reasoning budget a paying subscriber could rent today, which is part of why the claim unsettled people who track the field closely. The skepticism isn't about whether the model produced output. It's about what that output is worth before a human checks it. Francesco Maggi, a mathematician at UT Austin, raised this point publicly when OpenAI previewed an earlier, smaller batch of proofs: mathematics doesn't grow just by piling up correct statements. Results have to be understood, connected, explained, challenged, and reused by other mathematicians, Maggi argued, or they risk remaining what he called, in Italian, "lettera morta." Dead letters. Technically valid, functionally inert. [OpenAI drops 722 AI math proofs and mathematicians are not impressed](https://startupfortune.com/openai-drops-722-ai-math-proofs-and-mathematicians-are-not-impressed/) OpenAI's October 6 GitHub dump spans 372 research families and roughly 4,000 posed problems, each surviving result costing about three hours of ChatGPT Pro compute, following a Navier-Stokes claim that triggered a 25-signature Fields Medalist rebuke over credit and review. - [openai releases 722 mathematical proofs via github](https://startupfortune.com/openai-drops-722-ai-math-proofs-and-mathematicians-are-not-impressed/) - [mathematicians criticize openai ai generated math proofs](https://startupfortune.com/openai-drops-722-ai-math-proofs-and-mathematicians-are-not-impressed/) That's the real fight over this release. A proof that compiles in a formal verifier like Lean is binary: it checks or it doesn't. But compiling is not the same as being understood. Several of OpenAI's past results, including a disproof of the 80-year-old Erdős unit distance conjecture announced in May, drew comment from mathematicians who said they could verify the logic step by step without being able to explain why it worked, or what idea actually cracked the problem. A proof nobody can explain is still a proof. It just isn't yet knowledge anyone can build on. There's also a harder question sitting underneath all of this: what counts as "solving" a problem on one of these catalogs in the first place. Open-problem lists compiled over decades often contain items at wildly different levels of difficulty, some genuinely open and brutal, some closer to known-but-unpublished folklore. OpenAI has not released a side-by-side breakdown showing which of the 90 are full resolutions of long-standing conjectures versus narrower technical advances that happen to close out a listed item. Until that breakdown exists, outside mathematicians are left auditing the GitHub repository problem by problem, rather than taking the headline figure on its face. ## Why OpenAI is being more careful this time This release didn't happen in a vacuum. On September 21, after OpenAI said an internal model had resolved more than 100 long-standing open problems, including a version of Navier-Stokes, the company formed a nine-member Advisory Group on Mathematics and Artificial Intelligence, hosted by the Institute for Advanced Study in Princeton. The roster includes Timothy Gowers, Martin Hairer, Edward Witten, and Ravi Vakil, among others, people whose names carry weight specifically because they aren't OpenAI employees. That September claim also came with a credit dispute attached. NYU mathematician Tristan Buckmaster and Anthropic researcher Levent Alpöge, who had been working on related fluid-dynamics problems with their own AI tooling, alleged that OpenAI became aware of their unpublished work and moved quickly on a related result, according to reporting from Axios. OpenAI disputed the characterization, but the episode is part of why this week's release leans so hard on transparency: a public repository, machine-checkable files, and an advisory board that didn't exist for the company's earlier claims. None of that settles whether 90 of 500 is the right way to describe what happened. It does mean that, for the first time, outside mathematicians have something concrete to check instead of a press release and a number. That's a real shift from August, when OpenAI's Astra model claimed ten decade-old problems solved and shipped Lean certificates for review, a release that was itself an attempt to answer criticism of the May conjecture disproof. Each round has added more verification infrastructure. None of them has yet produced a consensus from the mathematics community that the underlying claims hold up at the scale OpenAI is describing. **Also read:** [McDonald's AI pricing engine faces lawsuit and antitrust questions](https://startupfortune.com/mcdonalds-ai-pricing-engine-faces-lawsuit-and-antitrust-questions/) • [AI Music Cannot Be Copyrighted, So Creators Need Another Way to Prove It Is Theirs](https://startupfortune.com/ai-music-cannot-be-copyrighted-so-creators-need-another-way-to-prove-it-is-theirs/) • [Henri Kagan and Kenso Soai win the 2026 Nobel Prize in chemistry](https://startupfortune.com/henri-kagan-and-kenso-soai-win-the-2026-nobel-prize-in-chemistry/) [OpenAI's math model is reportedly chipping away at two more Millennium Prize problems](https://startupfortune.com/openais-math-model-is-reportedly-chipping-away-at-two-more-millennium-prize-problems/) Reddit threads on r/OpenAI claim the company's model is advancing on two more $1 million Clay Institute problems, weeks after its contested Navier-Stokes result drew a 25-signature Fields Medalist rebuke. - [openai math model solving millennium prize problems](https://startupfortune.com/openais-math-model-is-reportedly-chipping-away-at-two-more-millennium-prize-problems/) - [how openai ai is cracking unsolved mathematics](https://startupfortune.com/openais-math-model-is-reportedly-chipping-away-at-two-more-millennium-prize-problems/) *This article is posted in [AI News](https://startupfortune.com/category/ai/), check it out for more related stories.* ## Join the discussion [Open in the community →](https://startupfortune.com/community/) Almost there. 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