Measure the task, not just the token
A low token price is only one part of an AI system’s cost. Retries, tool calls, long responses, and manual corrections all add up. For a document-processing workflow, compare the total cost of a correctly completed document rather than the cost of one request. Define a representative test set before changing models.
Match model capacity to the job
Use a routing policy based on task difficulty and risk. A compact model may handle classification or extraction; ambiguous cases can escalate to a more capable model or a human. Routing is an engineering hypothesis to test, not a promise that every smaller model will work.
Remove work before buying cheaper work
Trim irrelevant context, retrieve the passages needed for the question, and avoid requesting lengthy output when structured fields are sufficient. Measure quality and end-to-end latency together. Optimizing one stage can shift costs elsewhere.
A practical starting point
Choose one repeatable workflow. Record completion quality, retries, human correction time, latency, and total model charges. Compare two configurations against the same cases and set a quality floor before selecting the lower-cost option.