Social media reports claim that OpenAI has recently completed a new round of pre-training internally codenamed "Bel," with total parameters exceeding 100 trillion; OpenAI has not confirmed the existence of Bel, the parameter scale, or the completion time of the training.

The reports describe Bel as the successor pre-training base to "Doug." According to this claim, Doug is expected to support Astra and GPT-6 level systems after further reinforcement learning, while Bel is positioned as the next-generation foundational model following that route.

The statement about "over 100 trillion parameters" does not disclose model architecture, the number of active parameters, expert mixture routing methods, training token scale, training set composition, training cluster scale, or training costs. Therefore, total parameters cannot be directly equated to the parameters actually used in a single inference call, nor can the model's capabilities be judged based on this.

Reports suggest that Bel may form a model after GPT-6 through reinforcement learning and could become the basis for the so-called "AGI threshold model"; the "AGI threshold" is not a product specification, model name, or verifiable performance metric publicly released by OpenAI, but rather an internally rumored positioning.

OpenAI has publicly confirmed that Astra is one of its "next major models," but has not announced a release date, price, model ID, or complete technical specifications. The company has stated that some planned cutting-edge reinforcement learning training is on hold due to cybersecurity risk assessments; this means that even if a pre-training has been completed, it cannot be inferred that related products will be released soon.

Regarding Anthropic's claim that "it is difficult to produce a model of the same level this year, mainly limited by computing power," this also originates from unverified social media reports. Currently, there is no official statement from Anthropic, disclosure of computing power procurement, or independent technical reports to prove that it cannot train or release a model of the same level as Astra.

In market mechanisms, such rumors themselves will raise expectations for cutting-edge training computing power, HBM memory, network interconnections, data center power, and model post-training data; the narrative benefits are concentrated on chip, cloud service, AI power, and data suppliers. The actual flow of funds still depends on whether OpenAI discloses training scale, deployment plans, and commercialization paths; competing companies bear the narrative pressure of "computing power lagging behind," but there is no verifiable evidence that the competitive landscape has been rewritten by Bel.

Source: Public Information