Ted Sanders explains OpenAI model naming: version numbers signal release order, lineage and size of leap

Ted Sanders has laid out how OpenAI's model naming works: a version number signals release order, relationship to other releases and the size of the improvement, and models are ordered by tier as Luna < Terra < Sol < Astra. The explanation was collected as a quotation by Simon Willison on his weblog on 28 October 2026.

What Sanders said

Sanders starts from the point that LLM performance is "incredibly multidimensional." In his words, there is no guarantee of a fixed ordering in which the model that is best at math is also the best at jokes.

Despite that, he says much of the variation in performance is explained by a single variable: model size. According to Sanders, bigger models will in general:

• be more performant and reliable
• cost more per token
• have better recall of esoteric facts

Replying to a point raised by the person he was answering ("As you note"), he states that the models are ordered Luna < Terra < Sol < Astra. The quotation is excerpted, with the rest of that passage elided.

How version numbers work

Sanders says version numbers now signify three things at once:

1. Order of release. His example: 6.1 would have launched after 5.6.
2. Relationship to other releases. Models sharing a version, such as the 6.1 models, may share similarities in training data and personality. He gives this as the reason a release is not numbered 5.7.
3. Magnitude of improvement. A 6.1 is "not a giant enough leap to merit the headlines and expectations that would come with a 7."

The quotation gives no parameter counts, prices, context windows or benchmark scores for any model, and it names no specific release date.

What this means

The explanation separates two axes that are easy to conflate. Tier names (Luna, Terra, Sol, Astra) track relative size, and per Sanders size is the main driver of reliability, per-token cost and factual recall. Version numbers track time and lineage. A higher version number does not by itself mean a larger or more expensive model, and a lower number does not mean a weaker tier.

For engineers choosing between models, the practical reading follows from Sanders's own framing. Version tells you how a model relates to its siblings and how big a step it represents. Tier tells you where it sits on the size, cost and recall scale. Sanders also cautions that performance is multidimensional, so tier ordering is a general tendency and not a guarantee for every task. Model selection still needs task-specific evaluation.

The point about a "7" being reserved for a large leap is a statement of intent from Sanders about expectations. A minor increment like 6.1 is meant to signal shared lineage and a smaller improvement, not a headline-level jump.