It doesn't work well in practice.
Try it yourself, use a big model like Opus or Sol to implement everything by first making a plan using plan mode.
Then try distributing the task to a cheaper models like Luna Max or Gemini Flash 3.8.
During planning, the big model already reads the relevant files in context, while giving a smaller model a slice of work itself requires the big model to reason about the task distribution, review, etc.
So do you really save on tokens?
When I've tried it using API-rate billing I've saved on $$ on the tasks where I split planning+execution into Sol+Terra or Terra+Luna even. I wasn't paying attention to the token count, I was paying attention to the spend.
> Try it yourself, use a big model like Opus or Sol to implement everything by first making a plan using plan mode.
When I do this, I can have it use cheap subagents with models like Luna to read the relevant files.
Do you have the cheap models summarize the files? How do they get the relevant information to the bigger models?
Maybe not, but I like to review the plan anyway so that I'm less surprised by what it actually did.