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| | 阿莫迪偷师姚顺雨，奥特曼偷师梁文锋 | | | 字母AI ｜ 2026-09-26 | | | |
| 声明： 本消息或因风格和篇幅原因进行过编辑，但未经核实，也不代表我们的立场、观点或建议。如有侵权，联系秒删。[ 使用条款 ] |
scroll 0 1 正所谓天下文章一大抄，看你会抄不会抄。
刚刚发布的Opus 5.5和GPT-6 Luna，一眼望去，这两个模型上面满满是姚顺雨和梁文锋的影子。
或许是因为两家公司都已经将预训练做到了极致，为了提升模型的智能以及降低算力成本，所以这两个新模型都选择开辟新的战场。
Opus 5.5是对上下文动手，GPT-6 Luna是对缓存动手。
这我可熟啊！一个混元，一个DeepSeek啊！
但你别说，阿莫迪和奥特曼还真抄得挺像那么回事。
01
阿莫迪偷师姚顺雨
先看一组数字，Artificial Analysis的智能指数里，有一项统计是“跑完一道题平均要输出多少token”。
在这张榜单上，Claude Opus 5.5在max档位下，平均每题要吐出11.9万个输出token，其中8.4万个是“思考”，3.5万个是最终答案。而OpenAI的GPT-6 Astra，在同一档位下只用了2.7万个 token。
大模型想要提升模型的智能水平，传统路线是在预训练阶段堆数据、堆算力，把智力放进模型的权重里，一次训练、反复使用。
这条路线的模型在推理时几乎不做额外思考。模型训练时数据价值越高，模型的智能水平就越高。
但传统路线就怕出现没见过的题型，传统模型的泛化是插值。在训练数据覆盖的分布附近，它表现很好。一旦碰到没见过的题型，或者需要多步推理的题，它就开始瞎编。因为它的“智力”全在权重里，权重没见过的模式，它推理时也补不出来。
就像你对着木桩练了很久的拳法，结果发现上了战场以后，对方不会站着不动，而且还会使用武器。
另一条是test-time路线。训练完的模型只是个半成品，真正答题时，先让它写一大段推理，想得越久、答得越好。智力不再只来自权重，还来自于每次调用时现场的推理。
换句话说，智力被搬进了上下文。
Opus 5.5走的就是test-time路线。
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官方有这么一条声明：除非特别注明，所有成绩都跑在“adaptive thinking at max effort”（最大力度的自适应思考）下。 0 | | | |
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点击朱笔，直抒胸臆