Simon WillisonOct 29, 2026 07:23

Ted Sanders explains OpenAI's model naming conventions and the role of model size

Ted Sanders, likely an OpenAI insider, explains that LLM performance is highly multidimensional but that model size is the single variable explaining most variation. He outlines OpenAI's naming scheme where version numbers indicate release order, family relationships, and improvement magnitude—for example, 6.1 follows 5.6 and shares traits with other 6.1 models, while a jump to 7 would signal a major leap. The quote also notes that larger models (ordered Luna < Terra < Sol < Astra) are generally more performant, cost more per token, and have better recall of esoteric facts.

AI Brief

Offers rare direct insight into OpenAI's internal reasoning for model naming and the practical trade-offs of model scaling.

In general, LLM performance is incredibly multidimensional. There's no guarantee of a fixed ordering where the model that is best at math is also the model that's best at jokes. Despite that complexity, much of the variation in performance is explained by just a single variable: model size. In general, models that are bigger will (a) be more performant / reliable, (b) cost more per token, and (c) have better recall of esoteric facts. As you note, our models are ordered:

Luna < Terra < Sol < Astra

[...] These days, version number signifies a few things at once: the order of release, the relationship to other releases, and the magnitude of improvement. So something like 6.1 would indicate it was launched after 5.6, may share some similarities to other 6.1 models in terms of training data / personality (which is why it's not 5.7), and isn't a giant enough leap to merit the headlines and expectations that would come with a 7.

— Ted Sanders, explaining OpenAI's model naming conventions

Tags: generative-ai, gpt, openai, ai, llms, ted-sanders

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Ted Sanders explains OpenAI's model naming conventions and the role of model size

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