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Kimi K2.7-Code claims 30% fewer thinking tokens and a drop-in API swap path, but independent benchmarks show kernel ...

I switched for speed and stayed for everything else.

正确的做法是聚焦：在一个具体场景里，用私有数据训练专属模型，在真正重要的地方超过通用模型，这个专属模型本身就成了护城河。反过来，如果去和前沿模型比拼通用能力，结局大概率不是成为赢家，而是被某个算力充足的大公司收购。