OpenAI解读GPT模型命名与选择
OpenAI 的 Ted Sanders 表示，LLM 性能具有多维特征，数学能力最强的模型未必也最擅长讲笑话。模型规模通常与性能、可靠性及冷门知识 recall 能力相关，但更大的模型每个 token 的成本也更高。OpenAI 的模型顺序为 Luna < Terra < Sol < Astra，不过更好的训练数据或架构可能让较新的小模型超过旧的大模型。
这些说明帮助用户理解 GPT 模型命名，并在质量、可靠性和 token 成本之间做选择，同时表明单一排名或 IQ 分数无法概括所有能力。
A quote from Ted Sanders
Simon Willison’s Weblog
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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.