Why pushing people onto the cheapest model often raises the total cost of AI work, and how to tell when paying more per call saves money.
A model that costs a tenth as much per token looks like an easy saving. On simple work it often is. On hard work it frequently is not, because the cheaper model needs more attempts and returns answers that a person has to fix. The price list measures the cost of one call. It says nothing about how many calls the work will take or who has to clean up afterwards.
Faced with a rising AI bill, many companies hold everyone to a cheaper model. The invoice falls. Then the hard tasks start taking three or four attempts, people spend more time reviewing output, and much of the saving is consumed by extra volume and labor that nobody attributes to the policy.
The opposite policy fails too. Letting everyone use the strongest model for everything overpays on the many simple steps where a cheaper model would have produced the same result.
The cheapest model wins on some steps and loses on others. Within a single piece of work, planning a change might need a strong model while carrying out the edits needs a fast one. The decision has to be made per step, on the expected cost to finish, not once per user or once per company.
CompletionPrism™ makes that call before each step is sent. When a stronger model will cost less to finish because it avoids rework, it routes up and says so on the receipt, including the extra token spend, printed as a negative line. A tool that only ever reports savings is a marketing number, not a measurement.
Pick ten hard tasks your team did last month. For each one, count the attempts and estimate the minutes spent fixing the result. Multiply by a real hourly rate. If that number is larger than the token bill for the same tasks, your cheapest model is costing you money.