Ted Sanders is being quoted.
OpenAI model names reveal release order, related versions, and expected change, while model size shapes performance, reliability, and cost.
Key Takeaways
- OpenAI model names signal a hierarchy—Luna < Terra < Sol < Astra—helping readers infer general capability, reliability, and likely token cost.
- Model size is a major driver of performance: larger models tend to be more reliable and better at rare facts, but cost more per token.
- Version numbers communicate release order, relationship to other models, and expected magnitude of improvement, such as 6.1 following 5.6.
- AI performance is multidimensional, so naming conventions are a useful reading guide, not a substitute for task-specific testing or benchmarks.
Meta description: Learn how OpenAI model naming conventions signal release order, similarity, and improvement, and why model size drives performance, reliability, and cost.
Tags: OpenAI, LLMs, model naming, generative AI
Featured image: abstract, friendly illustration of tiered model steps and release markers, no real people, no logos, no text.
For readers comparing large language models, OpenAI model naming conventions explain how lineup order and version numbers can signal expected capability, cost, and release meaning. The source, based on Ted Sanders, does not promise one benchmark score. It gives a practical way to read model names.
Performance is multidimensional
LLM performance is incredibly multidimensional. There is no guarantee that the model best at math is also best at jokes. Still, much of the variation is explained by one variable: model size. Bigger models are generally more performant and reliable, cost more per token, and have better recall of esoteric facts.
This creates a useful tradeoff. If a task needs reliability or rare facts, a larger model may help. If cost per token matters, a larger model may not be the best choice. The name alone does not settle the question, because different tasks behave differently.
The OpenAI model order
The source gives an ordering: Luna < Terra < Sol < Astra. This ladder suggests a general hierarchy. Astra sits above Sol, Sol above Terra, and Terra above Luna. It does not mean every capability is identical. It does suggest where a model sits in the provider's lineup.
For readers, that order is a simple comparison path. A higher tier may be expected to deliver more reliable performance and broader factual recall, but it also points toward higher token cost. A lower tier may be enough for common tasks where cost and speed matter more than deep recall.
What version numbers communicate
Ted Sanders explains that a version number signifies several things at once: order of release, relationship to other releases, and magnitude of improvement.
The example is 6.1. It indicates the model was launched after 5.6. It may share similarities with other 6.1 models in terms of training data or personality. That relationship is why the source says it is not labeled 5.7.
The number also sets expectations. A 6.1 release is described as not a giant enough leap to merit the headlines and expectations that would come with a 7. So the version is not just a timestamp. It tells users how the release relates to earlier models and how much change they should expect.
How to use this when reading AI news
When a new model release appears, readers can ask three questions. Where does it sit in the model order? If the lineup is Luna < Terra < Sol < Astra, position can hint at general capability and cost. What version number does it use? If 6.1 follows 5.6, that shows release order and possible relation to the 6 line. How big is the change? The source suggests 6.1 does not carry the expectations of a 7.
This framework does not replace testing. It does not identify the best model for jokes, math, coding, or factual recall. The source itself warns that performance is multidimensional. But it makes OpenAI model naming conventions more readable.
Who should use this naming guide
Readers who want a quick way to interpret model tiers and version numbers can use this guide as a starting point. It is especially useful for people comparing OpenAI models by expected cost, reliability, and release position.
Readers who need a precise benchmark should skip relying on names alone. The source does not provide test scores, task rankings, or guarantees about a model's performance on every job. It explains naming logic, not universal capability.
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