OpenAI’s Internal Model Can Propose Solutions to Millennium Prize Problems? First, Look at What This Tweet Omits

Haider posted a tweet on X, and it carries a lot of information. The tweet claims that OpenAI has an internal model capable of proposing solutions to the Millennium Prize Problems; internally there is also Astra 6.1, which was not released due to alignment issues but represents a major capability jump. Haider’s judgment is that the outside world underestimates OpenAI, and the sense of falling behind comes from release cycles.

The image places OpenAI and Anthropic in a circular flowchart, with both bearing the tagline “the world’s strongest model.” Red text on the left says “you are here now,” pointing to Anthropic. The meaning is straightforward: outside observers see only one frame of the release process; wherever a company has just released a model, that is where attention stops.

This claim needs to be broken down. The Millennium Prize Problems are seven mathematical problems, with the Clay Mathematics Institute offering a million-dollar prize for each. Proposing a solution is very far from a verified proof. Mathematical proofs require peer review, and model outputs need to be reproducible and checkable. Between an internal model and a public product, there are also release, safety evaluation, and alignment review. The claim about Astra 6.1 currently has no public paper, evaluation, or interface, so outsiders cannot test it.

Someone in the replies made this clear. TestedAI said that the big claim has no public materials to check, and that proposing a solution to the Millennium Prize Problems is far from a verified proof; without a paper or release, it can only be considered an internal rumor and cannot be tested externally. Tim B. added that Google is currently the best, with Anthropic behind. The two replies pull the discussion from taking sides back to evidence and currently available products.

If the tweet is true, what is worth watching is the time lag between the internal versions at leading labs and the public versions. Alignment issues may slow releases, and may also make capability demonstrations more cautious. The outside world often uses “who just released the strongest model again” to judge rankings, and release cadence can easily be read as a technology gap. For developers, internal rumors cannot be treated as API capabilities; for ordinary users, a publicly available model is the only model that can actually be used.

This tweet is worth reading, but don’t treat it as a conclusion. Solutions to the Millennium Prize Problems must wait for papers and verification, and Astra 6.1 must wait for a release or evaluation. Without those, it is at most an internal rumor. The original tweet is here: Haider’s tweet.