Model switching savings calculator
Same workload, two models, one number. Set your token mix and volume once, pick what you run today and what you are considering, and see what the move is worth over a month and a year.
How this is calculated
Both models are priced against the identical token mix and request volume, so the only variable is the rate card. Selecting a discount applies it to whichever of the two models publishes one, which is deliberate: if one model offers caching or batch pricing and the other does not, that is a genuine part of the cost difference and hiding it would flatter the model without the discount.
What the number does and does not tell you
It tells you the size of the prize, which is the input you need before deciding whether the evaluation work is worth doing at all. A $40-a-month difference does not justify a week of prompt migration; a $40,000-a-year difference justifies quite a lot of it.
It does not tell you whether the cheaper model is good enough for your task, and nothing on a pricing site can. The failure mode to watch for is a cheaper model that quietly produces longer outputs: if it generates 40% more tokens to say the same thing, a good part of the headline saving disappears before you have evaluated a single response for quality. Measure output length on real traffic before trusting the projection.
If the two models you are weighing are among the popular pairings, the head-to-head pages carry a fuller comparison: see all model comparisons.
Questions about switching models
How much can switching LLM models save?
Frequently an order of magnitude, and occasionally two. The spread between the cheapest and most expensive models tracked here is enormous, and a lot of production traffic runs on a flagship model doing work a mid-tier model would handle identically. The saving is only real if output quality holds, which is why this calculator gives you the number and not the recommendation.
Should I always pick the cheaper model?
No. A cheaper model that needs two attempts, or that produces output someone has to correct, is more expensive than the model that got it right first time, and the correction cost does not show up on the API bill. Use the saving figure as a budget for evaluating the swap, not as the conclusion.
How do I evaluate a model switch properly?
Take a sample of real production traffic, run it through both models, and compare outputs on the dimension that actually matters for your task. Watch for the failure modes that cost money downstream: hallucinated fields in extraction, missed edge cases in classification, longer outputs that inflate the token count you were trying to reduce.
Does the token count stay the same when I switch models?
Not exactly. Different model families tokenise differently, so the same text can come out 10-35% longer or shorter depending on the pair. For a rough comparison the difference is usually within noise; for a tight budget, measure the token count on both models rather than assuming they match.
What about migration cost?
Real, but usually one-off: prompt tuning for the new model, re-running evaluations, updating anything that depends on output format. Compare it against the annual saving figure rather than the monthly one, since that is the horizon the work pays back over.