Did OpenAI Really Finish a 10-Trillion-Parameter Pretraining Run?

On August 25, a post on r/singularity claiming OpenAI had just finished pretraining a model with more than 10 trillion parameters rocketed to the top of the subreddit (859 upvotes, 257 comments). The story traces back to a single anonymous X account — @synthwavedd, "Leo" — whose self-described "SCOOP" said the run, codenamed "Bel," is expected to become the base model for OpenAI's Astra line and post-GPT-6 systems. OpenAI has not confirmed anything: no blog post, no paper, no comment to the press. As of this writing, the entire story rests on one social media post.

A scoop with a single source

Leo posted the claim on August 25. The account has no documented track record of accurate OpenAI leaks that mainstream outlets have vouched for, and the details are, by the poster's own framing, speculative:

• Codename: "Bel," described as the successor to an earlier run codenamed "Doug"
• Scale: more than 10 trillion total parameters, "similar in size to GPT-4.5"
• Purpose: base for Astra and GPT-6-class models after further reinforcement learning
• Source: one X post by @synthwavedd, August 25, 2026
• Confirmation: none from OpenAI; no technical specifications released

CryptoBriefing and Wccftech both picked the story up the same day, and both were careful to flag the absence of official validation. The leak also carries convenient details that cannot be checked: that OpenAI believes Anthropic has no compute to answer Astra, and that Anthropic privately expects to retake the lead early next year. Self-serving claims about competitors are exactly the kind of garnish an anonymous source adds for credibility — and exactly the kind a journalist should discount.

The drought that makes it plausible

The rumor spread fast because it fills a real, widely reported narrative hole. OpenAI has not shipped a new frontier base model since GPT-4o in May 2024; o1, o3, and the GPT-5 family were largely post-training and reinforcement-learning work layered on a GPT-4o-era foundation. Reporting around the company describes interrupted runs, safety-related pauses, and an internal codename ladder — Spud, Garlic, Doug — that reads like a trail of experiments that did or did not pan out. Astra, the model class "Bel" is supposed to underpin, was itself reported by The Information to be delayed over cybersecurity concerns, with a possible November release.

That makes the claim feel coherent. Plausibility, however, is not evidence. The same grapevine produced Orion, reportedly downgraded for underperformance, and the parameters count itself deserves scrutiny: "10 trillion parameters" and "10 trillion training tokens" are different quantities, and the two have already been mashed together in public discussion of OpenAI's plans. A rumor that matches the narrative is still a rumor.

What a 10-trillion-parameter run would cost

If the claim were true, it would be expensive in a way that is hard to overstate. Industry estimates put GPT-4 around 1.8 trillion total parameters, so a >10T model would be roughly five times larger — trained on clusters of tens of thousands of accelerators over months, at a cost that industry analysts routinely estimate in the billions of dollars for runs of this class. And pretraining is only the beginning: the model is useless until post-training and RL turn it into a product, adding months and more compute on top.

That is the uncomfortable part of the scale-economics debate this rumor reopens. The o-series already showed that inference-time scaling can buy real capability gains on top of an existing base — which raises the question of what a fresh, vastly larger base actually adds, and at what marginal cost.

Is bigger still better?

The surrounding evidence this week argues against blind faith in scale. Anthropic's most capable model, Claude Fable 5, is simultaneously its slowest seller — enterprises declining to pay a 2x premium for the best benchmark scores (our analysis). Meanwhile, capable models now run on consumer hardware with wide memory bandwidth, like the M5 Ultra's 1.2 TB/s (more here), compressing what used to require a datacenter into a desk. If customers won't pay for the best model today, the business case for a multi-billion-dollar training run — one that makes the current frontier "look like a primitive product," per the leak — gets harder, not easier, to defend. SemiAnalysis has long argued scaling laws still work. But "still work" and "still pay off" are different claims, and only one of them is testable in a market.

What would settle this

Three things to watch. First, whether OpenAI says anything at all — a paper, a blog post, or even a denial would be information. Second, whether Astra actually ships, and how far its behavior outruns the current generation; if Bel is real, its post-trained descendants should show it. Third, how Anthropic responds on compute and product, since the leak explicitly puts the two companies on a collision course. Until any of that happens, "Bel" should be treated like every other anonymous leak: interesting, unverifiable, and worth exactly as much as the single account behind it.