2026年10月28日 (中文(简体) ).
总的来说，LLM的性能是不可思议的多层面的。 固定的订单无法保证，其中最擅长数学的模型也是最擅长笑话的模型。 尽管如此复杂，但性能变化的很多原因只有一个变量：模型大小。 一般而言，更大的模型将(a) 表现得更好/更可靠，(b) 每件物品的成本更高，(c) 更好地回顾一些隐秘的事实。 正如你所指出，我们的型号是：
月球 < Terra < 索尔 < Astra
[...] 这些天，版本号同时表示一些事情：发布顺序，与其他发布的关系，以及改进的程度。 因此，类似6.1的东西会表明它是5.6之后的发布，可能与其他6.1模型在培训数据/个性方面有一些相似之处(这就是为什么它不是5.7)，并且不是一个巨大的跃进，不足以值得头条和预期，而伴随着7。
——泰德·桑德斯，解释OpenAI的模型命名惯例。
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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
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