Quoting Ted Sanders
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
阅读原始来源
来源记录
研究分析
来源记录
来源描述：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
现有背景
可供比较的近期同类记录：“A Bit about Email” (Hacker News, 2026年11月9日)；“Outlook.com is rejecting email from domains without DMARC” (Hacker News, 2026年11月8日)。主题相近仅用于导航，未核验其是否属于同一需求或独立证据。
研究状态
这条记录尚不能可靠归入主类，未生成商业或性能结论。
缺失证据
需要核验主要事件、受影响对象与原始证据。
下一步
阅读原文，明确它支持什么决策，再选择对应分析框架。