Search “OpenAI”

OpenAI doubles usage limits for multiple paid plans

On October 30, 2026, OpenAI announced that it would double some usage limits for Pro 100, Pro 200, Pro 500, and Business Premium. A Tibo post cited by Yuchen Jin that day said that usage caps would be doubled for all Pro 100/200/500 tiers and Business Premium, that the usage cap for Pro 200 would no longer be lowered, and that Work and Codex usage would remain unchanged. On the same day, Vaibhav Srivastav further clarified that users who had already subscribed to Pro 200 would retain their existing Work and Codex usage allowances, which would not be reduced. heise online reported on the same day that Pro 200 in practice only withdrew the previously planned limit reduction, and that Work and Codex usage remained unchanged.

View story:OpenAI doubles the usage limits for Pro 100/200/500 and Business Premium

OpenAI launches GPT-6.1 Terra on ChatGPT Work and Codex

On October 27, 2026, OpenAI released and launched GPT-6.1 Terra, touted for its speed and efficiency, visual reasoning, and precise computer operation, with clearer and more natural writing as well, rolling it out that day to Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex. On the same day, Arena announced that the model had entered Agent Arena and Code Arena; OpenRouter launched the model, saying it matches GPT-6 Astra on Agents' Last Exam at one-tenth the price per token; OpenClaw also announced that the model is now selectable. OpenAI co-founder Greg Brockman said Terra's computer use has crossed the threshold of a qualitative change and that using it is faster than operating a computer on his own. fal, meanwhile, used the fal Codex plugin to test turning Blender 3D content into Seedance 2.5 cinematic footage. On October 28, Arena announced that Terra ranked 14th in Code Arena: WebDev with a score of 1671, at a median cost of $0.40 per task, 28% lower than GPT-5.6 Terra. OpenAI's system card confirms that Terra is overall safer than older models, but its flag rates on items such as concealing uncertainty, circumventing restrictions, data exfiltration, and destructive operations

OpenAI releases 722 mathematics papers all at once

On October 23, 2026, Terence Tao and this year's newly minted Fields Medalist Deng Yu discussed the future of AI and mathematics in a conversation on the SAIR Foundation podcast, touching on the controversy sparked by OpenAI's mass release of AI-generated mathematical results. Tao described this kind of bulk problem-solving output as a byproduct of sorts, one that could even be called "toxic waste." The two argued that the boundaries between solving problems and understanding them are diverging, maintained that AI results must come with explanations and traceable citations, and that mathematicians should be involved from the outset of a project, and introduced the Open Math OS project launched by SAIR, which first integrates existing open-source models and tools and hopes to produce its own model in a few months. On October 25, reports said OpenAI had released 722 mathematics papers at once, covering number theory, geometry, and theoretical computer science, claiming progress on problems including the Riemann hypothesis, Catalan's constant, and the Kakeya conjecture, without peer review; the reports also said Fields Medalist Hugo Duminil-Copin stated that 16 problems related to his research had been flattened by AI, and that some doctoral students faced having their research topics covered and their degrees affected.

View story:Terence Tao and Deng Yu in conversation on the future of AI and mathematics

OpenAI says its models helped discover critical PHP security vulnerabilities

On October 21, 2026, OpenAI said its models helped researchers discover critical security vulnerabilities in PHP as part of the Patch the Planet initiative. PHP is estimated to run about 70% of websites. OpenAI said that over the past two months, a coalition it led of maintainers, researchers, and security vendors prepared fixes and protections ahead of the vulnerability disclosure, because the fixes were complex and required significant changes to the PHP engine; all sites using PHP can now upgrade to a version that keeps them secure. On the same day, Ethan Mollick said the companies he engages with are struggling to keep up with the pace of patching because AI defense systems are finding vulnerabilities before attacks arrive. On October 22, OpenAI released a Codex skill for checking whether a website is affected by the vulnerability; users can point Codex at a specified repository and follow the guidance to complete the check.

OpenAI FDE lead Colin Jarvis shares how the team works

On October 23, 2026, Colin Jarvis, head of OpenAI's FDE team, shared the team's working approach in a blog post: the team works side by side with customers to solve the hardest problems and truly deploy AI into production environments. According to a same-day report by Wall Street See, about 15 FDEs worked with a large semiconductor customer to bring all use cases online within 3 to 6 months, saving about 48% of targeted testing time and improving median performance by 22%. After the Codex upgrade, the team converted custom applications into Codex-based plugins and skills, removing about 90% of the custom code for each project and shortening the time to extend the solution to a second team from two months to one day. There is currently no follow-up development.

OpenAI plans to release hundreds of mathematical results

On October 7, 2026, WIRED reported that OpenAI had told relevant parties it planned to release solutions to hundreds of unsolved mathematical problems all at once on GitHub on Tuesday, but the release time had not yet been determined at that point. Subsequently, on October 6, OpenAI released more than 700 manuscripts all at once, claiming to have solved hundreds of unsolved mathematical problems; The Verge reported that nearly 400 of these AI-generated mathematical results were distributed across more than 700 manuscripts, and OpenAI said that about 42% of the main results in the 719 manuscripts had been formalized in Lean. On October 8, Gary Marcus criticized the release report for not explaining details such as the models used, the process, and the failure rate. On October 9, the Human Mathematics Association called on mathematicians to stop cooperating with OpenAI, and Terence Tao reposted the statement. On October 10, OpenAI retracted three manuscripts because of a notation error; the error invalidated the argument in one manuscript and the constructions in two papers that depended on it, and at the same time it revised another 14 manuscripts.

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OpenAI discloses annualized revenue of about $50 billion to investors

OpenAI recently disclosed to investors that, as of the end of September, the company's annualized revenue scale was about $50 billion, lower than the $70 billion widely reported by the market at the end of last month. People familiar with the matter said the difference stems from different statistical calibers: Anthropic includes sales revenue generated by cloud partners in revenue, while OpenAI does not use this method. OpenAI also disclosed that overall revenue run-rate grew 77% in the third quarter, enterprise business revenue run-rate grew 107%, and it confidentially submitted listing documents in June this year. On October 10, Rohan Paul cited an FT report to further explain that OpenAI calculates based on net income after deducting the share of partners such as Microsoft, while Anthropic calculates based on gross sales through cloud partners; both methods comply with US GAAP; the report said the $70 billion ARR was not announced by the company itself, but was derived by investors after upward adjustment according to Anthropic's caliber.

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Rohan Paul says market volatility shows OpenAI and Anthropic influence

OpenAI recently disclosed to investors that, as of the end of September, the company's annualized revenue scale was about $50 billion, lower than the $70 billion widely reported by the market at the end of last month. People familiar with the matter said the difference stems from different statistical calibers: Anthropic includes sales revenue generated by cloud partners in revenue, while OpenAI does not use this method. OpenAI also disclosed that overall revenue run-rate grew 77% in the third quarter, enterprise business revenue run-rate grew 107%, and it confidentially submitted listing documents in June this year. On October 10, Rohan Paul cited an FT report to further explain that OpenAI calculates based on net income after deducting the share of partners such as Microsoft, while Anthropic calculates based on gross sales through cloud partners; both methods comply with US GAAP; the report said the $70 billion ARR was not announced by the company itself, but was derived by investors after upward adjustment according to Anthropic's caliber.

View story:OpenAI discloses annualized revenue of about $50 billion to investors

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Li Xinyi criticizes OpenAI and Anthropic for harming the mathematics community by using AI to solve problems

On October 3, 2026, Li Xinyi, associate professor at the Beijing International Center for Mathematical Research at Peking University and an alumnus of Peking University's Mathematics Department (Class of 2007), publicly criticized OpenAI and Anthropic for using AI to "solve" difficult mathematical problems. He believes that such results are "solved but not understood at all," and that the accompanying publicity will scare off young people who want to study mathematics and mislead funding decisions. Li Xinyi also revealed that a postdoc on his team's draft on an obscure conjecture highly overlapped with a cross-disciplinary paper generated by AI on arXiv, ultimately ending in joint authorship; he maintains that academic credit should only go to those who contribute to understanding. In addition, he pointed out that work in applied mathematics of the "combining A with B" type is more easily replaced by AI, whereas in fields such as geometry and topology that rely on spatial intuition, AI currently does not learn well. At present, this criticism was reported by Huxiu, and no other sources have cross-verified it.

Li Xinyi criticizes OpenAI and Anthropic for harming the mathematics community by using AI to solve problems

On October 3, 2026, Li Xinyi, associate professor at the Beijing International Center for Mathematical Research at Peking University and an alumnus of Peking University's Mathematics Department (Class of 2007), publicly criticized OpenAI and Anthropic for using AI to "solve" difficult mathematical problems. He believes that such results are "solved but not understood at all," and that the accompanying publicity will scare off young people who want to study mathematics and mislead funding decisions. Li Xinyi also revealed that a postdoc on his team's draft on an obscure conjecture highly overlapped with a cross-disciplinary paper generated by AI on arXiv, ultimately ending in joint authorship; he maintains that academic credit should only go to those who contribute to understanding. In addition, he pointed out that work in applied mathematics of the "combining A with B" type is more easily replaced by AI, whereas in fields such as geometry and topology that rely on spatial intuition, AI currently does not learn well. At present, this criticism was reported by Huxiu, and no other sources have cross-verified it.

View story:Li Xinyi criticizes OpenAI and Anthropic for harming the mathematics community by using AI to solve problems

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