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.
Four ways: (1) We are built specifically for Claude Code model routing. (2) We route at a subagent/subtask level. (3) We support on-device routing. (4) We have a built-in ML router trained specifically to route Claude Code subagent tasks. Its use is optional.
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.
With Google's most recent 12b param Gemma model, even Mac users with just 16gb of unified memory can offload some tasks on-device.