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Can you elaborate on this "Sonnet and Haiku are almost always less capability-per-dollar than open models"?

Yes - in short, open models like Deepseek, Mimo, Kimi, and GLM tend to complete tasks with less tokens and cost less per token than both Sonnet and Haiku. So those models are more cost efficient, and we often think of that as them having higher "capability-per-dollar" than Sonnet or Haiku.

Much of Claude Code's internal model routing ends up delegating tasks to Sonnet or Haiku, so by intercepting those calls and using open models instead, we often see better performance at a better price.

yeah, I get you now. but those are all Chinese hosted right? Don't think my company will enable us using them.

Many of them are produced by Chinese labs. Some, like Neomotron, are U.S. made. And we support inference providers in both the U.S. and overseas.

If geography is important, we can restrict which geos inference takes place in. And if you don't want to use Chinese-trained models, you can use others like Mistral, Neomotron, Google's, or OpenAI's.

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