Ted Sanders 解释 OpenAI 模型命名规则：Luna < Terra < Sol < Astra
Quoting Ted Sanders
AI 导读
OpenAI 的 Ted Sanders 解释，LLM 性能虽然高度多维，但大部分差异可由模型规模这一单一变量解释：模型越大，性能与可靠性越高、每 token 成本越高、对冷门事实的召回越好。
摘要与补充说明
他透露 OpenAI 模型按 Luna < Terra < Sol < Astra 排序。版本号同时表示发布顺序、与其他版本的关系和改进幅度，例如 6.1 表示发布于 5.6 之后、可能与其它 6.1 模型共享训练数据或风格（因此不叫 5.7），且提升幅度不足以配得上 7 所伴随的期待。
正文
28th October 2026
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