7h • 👋 Start here
One expensive model doing every task is probably not the end state.
A better setup looks more like a team.
Use the strongest intelligence for decisions that actually require it.
Then delegate the execution to cheaper models whenever they can do the job well enough.
That becomes increasingly important when an AI employee is working all day.
A small difference in the cost of one task doesn’t seem important.
Multiply it across thousands of actions and suddenly architecture affects margins.
The question stops being:
**“What’s the best model?”**
And becomes:
**“What’s the cheapest model that can reliably do this specific job?”**
How are you deciding which models handle which parts of your workflows?
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One expensive model doing every task is probably not the end state.
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