Set a narrow task boundary
Choose a repeatable task with an observable result. State its inputs, allowed tools and required output. Include difficult examples and cases where the model should ask for clarification.
Use a fixed set of cases so that each candidate receives the same work. Record the acceptance rule before reading the results. Separate a task's frequency from the cost of an error.
Measure the whole workflow
Record the exact model release and settings, context size, runtime and hardware. Count tool use, retries and the review needed to accept the result. Measure response time and resource use on that configuration.
Test the intended workload, including its busy periods. A short isolated response leaves questions about repeated use and concurrent work. Keep those tests separate in the record.
Decide when to use another route
Define which failures should trigger a stronger model or human review. Test that handover with the context and tools it needs. Count both routes when you compare the complete workflow's cost and accepted output.
Keep uncertain cases visible. A route that accepts an incorrect result can make the apparent saving misleading. Review false acceptance alongside failures that the system detects.
Compare on a stated basis
Consider smaller models for focused work and local deployment. Test lower resource use and acceptable quality as hypotheses for the exact configuration. This page contains no measured saving, minimum memory figure or recommended model size.
The open-weight models guide covers release, licence and deployment records. The Token Procurement guide connects accepted work and capacity to the purchase decision.
Record what remains unknown
This is a local method guide based on the essay's task-first comparison. Candidate models, task results and hardware measurements remain to be supplied. The selector's benchmark and allowance estimates establish no local deployment capacity.
Keep a dated record for each candidate before recommending it. Preserve the task cases and failed outputs so that a later model or runtime change can be checked against the same acceptance rule.