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A quote from Ted Sanders

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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.

&mdash; Ted Sanders , explaining OpenAI's model naming conventions

Posted 28th October 2026 at 11:23 pm

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This is a quotation collected by Simon Willison, posted on 28th October 2026 .

ai

2,300

openai

478

generative-ai

2,041

llms

2,006

gpt

135

ted-sanders

7

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/ 테드 샌더스 인용 테드 샌더스 인용 ( simonwillison.net ) Simon Willison · 4시간 전 · AI 모델 LLM 성능은 일반적으로 매우 다면적입니다. 수학을 가장 잘하는 모델이 유머를 가장 잘하는 모델이기도 하다는 보장은 없습니다. 이러한 복잡성에도 불구하고, 성능의 대부분의 차이는 단 하나의 변수, 즉 모델 크기로 설명됩니다. 일반적으로 모델 크기가 클수록 (a) 성능/신뢰성이 향상되고, (b) 토큰당 비용이 더 많이 들며, (c) 난해한 사실에 대한 기억력이 더 좋습니다. 언급하신 것처럼, 저희 모델은 다음과 같이 정렬되어 있습니다: Luna < Terra < Sol 원문 보기 → 공유 댓글 아직 댓글이 없습니다. 첫 댓글을 남겨보세요.