Routing every task to the strongest model is expensive. Simple, high-volume work can go to a free local model while the stronger model handles judgment-heavy cases.
why send simple work to the strongest model?
Because it becomes an expensive habit. Simple, high-volume tasks often do not need the same level of judgment as harder cases.
A free local model can handle the boring volume and reserve the stronger model for work where judgment actually matters.
what changes when model routing follows the work?
The stronger model stays focused on the cases that need it. Simple work moves to a free local model, which lowers cost across the routine workload.
The intended result is the same quality where it counts, with less spent on easy work.
is there a downside to using a free local model?
That is the open question. The tradeoff depends on whether simple, high-volume work truly stays simple enough for the local model.
The field note points toward testing the boundary instead of paying the strongest-model rate for every task.
Should every AI task use the strongest model?
Routing every task to the strongest model can become an expensive habit. Simple, high-volume work can go to a free local model, while judgment-heavy cases stay with the stronger model.
When should I use a free local model?
Use it for simple, high-volume work. The stronger model can handle cases where judgment actually matters.
Can local models lower AI costs without hurting quality?
They can lower cost across routine work when the task is simple enough. The goal is to preserve quality where it counts.