A free local model handled high-volume classification and extraction first, while a stronger model reviewed the difficult survivors. This lowered cost and latency while reserving judgment for cases that needed it.
why should the strongest model see every case?
High-volume classification and extraction can begin with a free local model. The stronger model then focuses on the cases that survive the first pass as difficult.
That routing keeps expensive judgment concentrated where it matters most.
what changed when we routed difficult cases separately?
The result was lower cost and lower latency. The stronger model spent its effort on difficult survivors instead of processing the full high-volume workload.
which cases deserve the expensive model?
The difficult cases deserve the stronger model. The first model acts as the initial classification and extraction layer, leaving the stronger model with the cases that need more judgment.
can a free local model handle the first pass?
In this field note, a free local model ran high-volume classification and extraction before the difficult survivors moved to a stronger model. The finding is about routing work according to difficulty.
How do you reduce LLM cost for high-volume classification?
Run a free local model through the initial classification and extraction pass. Send the difficult survivors to a stronger model so expensive judgment is applied to fewer cases.
When should a case be sent to a stronger AI model?
Send cases that survive the first pass as difficult. The stronger model can then focus its judgment where it actually matters.
Can local models handle high-volume extraction?
A free local model handled the high-volume classification and extraction pass in this field note. Difficult survivors were routed to a stronger model afterward.