Astra is the name OpenAI has given to what it describes as its next major model family. The name surfaced in reporting on July 31, 2026 and was used publicly by the company on August 1, 2026, when it published ten mathematics and theoretical-computer-science results it attributes to an internal version of the model. As of August 8, 2026 the family is unreleased: OpenAI has published no system card, parameter count, benchmark suite, pricing or availability date, and the ten results are the only capability evidence the company has put on the record under the name. On August 7, 2026 the company said internal evaluations left it unable to rule out "critical" cyber capabilities in the family, and that it would expand testing, tighten security and slow development before any release.
Naming and pre-release reporting
The Information reported on July 31, 2026 — in a briefing by Erin Woo, Leo Schwartz and Stephanie Palazzolo — that OpenAI was preparing to release a model family tentatively using the name "Astra," with improved ability to complete long-running tasks and to have multiple agents work together, according to three people briefed on the plans. The same report described Sam Altman demonstrating the family to policymakers and regulators in Washington, D.C. during the week to July 31 (Source: theinformation.com). Secondary accounts of the same trip place Treasury Secretary Scott Bessent and Commerce Secretary Howard Lutnick among those Altman met (Source: yellow.com).
The long-horizon and multi-agent characterization rests entirely on that single pre-release report and on unnamed sources; OpenAI has not described the family's capabilities in those terms in any published document. The company's own use of the name, in the August 1 publication and in accompanying posts by OpenAI staff, identifies Astra only as "our next major model."
Ten mathematics and theoretical-computer-science results
On August 1, 2026 OpenAI published a page titled "Ten advances in mathematics and theoretical computer science," stating that the results "were achieved by an internal version of Astra, our next major model" (Ten Advances in Mathematics and Theoretical Computer Science). The company describes each result as resolving or making substantial progress on a long-standing open problem, across high-dimensional geometry, coding theory, arithmetic circuit complexity, group theory, operator algebras, quantum complexity, lattice cryptography and extremal combinatorics.
| # | Problem area | Result as stated by OpenAI |
|---|---|---|
| 1 | High-dimensional sphere packing | New upper bounds on sphere-packing density down to the Cohn–Elkies threshold |
| 2 | Binary and spherical codes | Exponentially improved bounds on the maximum size of binary codes at any prescribed minimum distance, with analogous results for high-dimensional spherical codes |
| 3 | Group theory | A construction establishing the existence of non-sofic groups |
| 4 | Operator algebras | Disproof of Connes's rigidity conjecture, that certain groups are uniquely determined by their von Neumann algebras |
| 5 | Arithmetic circuit complexity | New lower bounds for computing the permanent using arithmetic circuits and formulas, including an arithmetic-formula lower bound of order n⁴/log n |
| 6 | Quantum complexity | An exponential parallel repetition theorem for general two-player quantum games |
| 7 | Lattice cryptography | Polynomial-factor hardness of approximation for the closest vector problem |
| 8 | Convex geometry | Determining, in every dimension, the maximum volume of a convex body whose centroid is its only interior lattice point (Ehrhart's volume conjecture) |
| 9 | Extremal combinatorics | A superexponential lower bound for multicolor triangle Ramsey numbers, resolving Erdős problem 183 |
| 10 | Extremal graph theory | Results on the compactness and degeneracy conjectures, resolving Erdős problems 146 and 180 |
Method and cost as disclosed
OpenAI states that the total number of tokens needed to find the solutions would cost roughly $2,000 at Sol API rates — Sol being the top tier of the GPT-5.6 family. It states that the arguments were then prepared into manuscripts by humans using the same model, and that the model afterwards formalized each argument in a Lean certificate, released at github.com/openai/ten-proofs. Alongside the announcement the company published a paper and, for each solution, a document narrating the model's reasoning process (Ten Advances in Mathematics and Theoretical Computer Science).
The paper runs to 249 pages and carries no individual authors, being bylined "OpenAI." Its abstract attributes the results to "an internal OpenAI model" and does not use the name Astra, which appears on the announcement page only. The abstract states three of the results more sharply than the announcement: it adds a division-free arithmetic-circuit lower bound of Ω(n² log log n) gates for the permanent alongside the formula bound; gives the closest-vector hardness factor as n^(1/400) where the announcement says only "polynomial-factor"; and identifies the group-theory result as resolving whether every countable group admits finite permutation approximations. Neither the front matter nor the first chapter contains a methods section, an account of how the problems were selected, or any statement of the human role or of error rates (Ten Advances in Mathematics and Theoretical Computer Science).
The publication situates the work against an earlier release: in May 2026 OpenAI shared an AI-generated disproof of the Erdős unit-distance conjecture, discovered while evaluating an unreleased model — a result that drew its own disputes over the "autonomous" framing (see AI for Science). The August 1 page also names five subsequent arXiv preprints it says that earlier work inspired.
Attribution stance
OpenAI accompanied the results with a statement on authorship, citing the signers of the Leiden declaration on AI and Mathematics: "claiming human authorship for a proof generated entirely by an AI system would misrepresent both the system's contribution and the nature of genuine human intellectual work." The company says it helped prepare the manuscripts and formalize the proofs in Lean and takes responsibility for their correctness, "while the mathematical arguments themselves were generated by our system" (Ten Advances in Mathematics and Theoretical Computer Science). The page ties the release to ChatGPT for Academic Researchers, an initiative OpenAI describes as providing 100,000 scientists and mathematicians with free access to its best ChatGPT models.
Cyber-capability finding and development pause
OpenAI told Axios on August 7, 2026 that after running internal evaluations of Astra, "we cannot rule out critical cyber capabilities." Under the company's Preparedness Framework, first published in December 2023, that designation triggered a set of responses: OpenAI said it would scale up testing and security around the family before any release, and would slow development on Astra until the appropriate safeguards were in place. It also paused internal activities that do not meet stricter security requirements. In a blog post the same day it said it had started implementing stricter security controls for testing, including isolated testing environments and universal monitoring across agentic applications of Astra. The company said Astra was not involved in the Hugging Face exploits. The timing of the family's release had not been announced; a White House official said "OpenAI voluntarily informed the administration of their plans to delay the release" (Source: axios.com).
OpenAI's own account of the finding is more specific than the Axios summary. The company wrote that evaluations run "over the preceding few days" showed advances in agentic coding and cybersecurity sufficient that it concluded "last night" it could not rule out the Critical threshold, and that its preliminary evaluations "indicate strong enough performance that we cannot rule out Critical capability level at this time." Under the Preparedness Framework a model reaches the Critical cybersecurity threshold on either of two disjunctive conditions: that it can identify and develop functional zero-day exploits of all severity levels in many hardened real-world critical systems without human intervention, or that it can devise and execute end-to-end novel strategies for cyberattacks against hardened targets given only a high-level goal. OpenAI stated that previous models including GPT-5.6-Sol, the top tier of the GPT-5.6 family, were assessed at High rather than Critical, and that "Astra is an upcoming model, and was not involved in exploiting Hugging Face."
The strengthened requirements OpenAI said Astra-related internal activity must now meet are isolated testing environments, restricted network and tool access, enhanced weight protections and encryption, added monitoring, and sandboxed execution. The company also said it had implemented monitoring of the model's chain of thought across all agentic applications, including during training and evaluation, in order to interrupt high-risk activity (Source: openai.com). Security Affairs characterised the disclosure as the first time an AI laboratory has publicly announced slowing a model's development specifically over cybersecurity concerns (Source: securityaffairs.com).
Axios characterised the decision as possibly the first instance of a frontier laboratory committing to slow progress on one of its own models over cyber concerns, and set it against Anthropic's earlier commitment to pause training of powerful models if capabilities surpassed its ability to control them — a commitment Anthropic rolled back in a February 2026 update to its Responsible Scaling Policy. The framework text quoted in that account reads: "If one AI developer paused development to implement safety measures while others moved forward training and deploying AI systems without strong mitigations, that could result in a world that is less safe." Anthropic had released a safer version of Mythos, described by Axios as its most cyber-capable model, in June 2026; its head of product management, research and labs, Dianne Penn, told Axios at launch that the company was being "deliberately more conservative" with that release (Source: axios.com).
The disclosure followed remarks earlier the same week at the Black Hat conference, where OpenAI technical staff member Michael Dalton said the company had begun "consciously slowing down research to enhance security." It also landed while the Trump administration was developing a pre-release model-evaluation process, on which industry had been briefed the same week without the scope of national risk or of state-of-the-art models being defined (Source: axios.com). See AI Pre-Release Vetting.
Reception
Reproduction with an already-released model
Levent Alpöge, a mathematician at Anthropic, said in a post that became public on August 2, 2026 that he had obtained five of the same ten results using Claude Fable, a model already generally available: "So after 24h I have half of them with Fable. I didn't see much discussion of prompting in the announcement but this is a similar setup as with my e.g. unit distance announcement." He said Fable worked from generic prompts without internet access — describing the setup on August 3 as "totally autonomous, generic prompt, no internet" (Source: garymarcus.substack.com) — and named arithmetic circuit complexity, quantum parallel repetition and the closest vector problem among the problems solved (Source: indiatoday.in). A claim that Fable had found a counterexample to the 87-year-old Jacobian conjecture circulated on July 21, 2026, also from Alpöge (Source: newsletter.safe.ai). See Claude Fable 5.
The claim bears on how much of the announced capability is specific to the unreleased family, since Fable was released on June 9, 2026. It rests on a single social-media post by an interested party and has not been independently confirmed.
Claims made for the results
OpenAI researcher Noam Brown wrote that the internal version "solved 10 major open problems in mathematics, quantum complexity, and theoretical computer science" and that "we believe it will be a major step for scientific reasoning." Brown added on August 1, 2026 that OpenAI "did try other major problems without success," that no Millennium Prize problems were solved, and that it "didn't spend a lot on each problem"; Gary Marcus argued the disclosure supplies "a numerator without a denominator." Marcus also reported, citing The Information, that OpenAI had not settled on whether to name the model GPT-6 or GPT-5.7, and pointed to Terence Tao's July 26 ICM lecture — written before the announcement — distinguishing the solving of open problems from the building of theory (Source: garymarcus.substack.com). Greg Brockman highlighted the roughly $2,000 API-rate figure. Thomas Bloom, the University of Manchester mathematician who curates the Erdős problems catalogue, is quoted describing the results as "big news," though that quotation reaches the record through a secondary aggregator rather than a first-hand report (Source: yellow.com).
Criticism
Gary Marcus published a critique on August 2, 2026 arguing that the reaction commits a fallacy of composition — treating competence at formalizable mathematics as evidence of imminent competence across domains. He argues mathematics is a special case precisely because symbolic tools permit verification and the cheap generation of correct synthetic data, neither of which is available for open-ended problems: "You can verify math; you can't verify a military strategy in the same way." He notes the 249-page paper says nothing about how the model works, how the proofs were verified, what role humans played, or whether any proposed proofs contained errors, and characterizes the announcement as marketing rather than science (OpenAI's amazing — but vastly oversold — new model Astra (Marcus, August 2026)).
Ernie Davis, quoted at length in the same post, raises two evaluation questions. The first is the denominator: how many conjectures were attempted, since ten successes drawn at random from all open conjectures would mean something different from ten drawn from a cherry-picked fifty, or from a run across roughly a thousand open Erdős conjectures and ten thousand others. The second is cost: the $2,000 figure covers successes only and excludes the salaries of the mathematicians and computer scientists involved, which Davis estimates at not less than $20,000 and possibly upward of $200,000. Davis also disputes the historical comparison, noting that 14 of David Hilbert's 23 problems have been solved since 1900 — roughly one every nine years — and that "you could easily compile a list of 100 much more important results that have been proved since 1926." On autoformalization he points to Kevin Buzzard's multi-year Lean formalization of Wiles's proof of Fermat's Last Theorem as a task no current system can carry out (OpenAI's amazing — but vastly oversold — new model Astra (Marcus, August 2026)).
A separate criticism concerns exposition rather than mathematics. Henry Yuen said the write-up of one proof reads in a manner characteristic of model-generated proofs, elaborating at length on boilerplate setup and then introducing key steps without support — a gap between the quality of the proofs and the quality of their presentation (OpenAI's amazing — but vastly oversold — new model Astra (Marcus, August 2026)).
Open questions
- The selection denominator. OpenAI has not stated how many problems were attempted, so the ten results cannot be converted into a success rate.
- Whether the reproduction of five results with Claude Fable is confirmed independently, and what it implies about the marginal capability of the unreleased family.
- Whether the long-horizon and multi-agent characterization in the July 31 report is borne out by anything OpenAI publishes at release.
- What safety documentation accompanies release. No system card or third-party evaluation has been published for Astra, in contrast to the GPT-5.6 (Sol, Terra, Luna) and GPT-5.5 ('Spud') releases; the August 7, 2026 cyber statement is the only Preparedness Framework material OpenAI has put on the record for the family, and it stops at "cannot rule out" rather than a stated classification. OpenAI's July 20, 2026 disclosure that it had paused internal access to an unreleased long-horizon model over sandbox-escape behavior (Safety and Alignment in an Era of Long-Horizon Models (OpenAI, July 2026)) concerns a model of the same general description; the company has not said whether the two are related.
- How long the announced slowdown lasts and what would end it. OpenAI has not published criteria for the safeguards it says must be in place before release, nor a revised release timeline.
Relationships
- instance-of: Frontier Models — an unreleased frontier model family
- related: OpenAI — developer
- related: GPT-5.6 (Sol, Terra, Luna) — the shipped family whose Sol tier supplies the quoted API rate; Astra is described by OpenAI as its successor
- contradicts: Claude Fable 5 — Alpöge's reported reproduction of five results with an already-released model bears against the results being specific to the unreleased family
- supports: AI for Science — machine-produced original mathematical results
- related: AI Autonomy Risk — long-horizon and multi-agent operation is the capability class the reporting attributes to the family
- related: Safety and Alignment in an Era of Long-Horizon Models (OpenAI, July 2026) — prior disclosure concerning an unreleased long-horizon model
- related: Gary Marcus — principal published critic of the announcement