Cost Explosion Simulator
One prompt change, priced at every user count from ten to ten million on a log slider.
The prices above are illustrative placeholders in the shape of a per-million rate, not anyone’s published price. Type in the two numbers from your own invoice before you quote the result.
$1,332 becomes $1,620 — 21.6% more, on 240,000 requests.
- Cost per request, before
- $0.0055
- Cost per request, after
- $0.0067
- Added per request
- $0.0012
- Requests per month
- 240,000
- Monthly bill, before
- $1,332
- Monthly bill, after
- $1,620
- Added per month
- $288.00
- Added per year
- $3,504
- Added per user per month
- $0.29
- Cost of 100 extra input tokens, per month
- $72.00
The arithmetic is deliberately trivial: added tokens × price ÷ 1,000,000 × requests. The point is the last term. A 400-token addition to a system prompt is a decision an engineer makes in about ninety seconds and nobody reviews, because at the scale it is tested — one person, a few dozen calls — it costs a fraction of a cent. The slider is the review. Drag it to your actual user count and the same edit is a number somebody has to approve.
Two asymmetries are worth reading off the breakdown. First, output tokens usually carry a much higher rate than input tokens, so an instruction that makes the model talk more is worth several times a longer prompt of the same length — put “think step by step” in the output field and compare. Second, the cost of 100 extra input tokens per month, on its own row, is the number to carry around: it converts a prompt diff directly into money without rerunning anything.
What this cannot tell you is whether the change was worth it. A longer prompt that removes a retry, shortens an output, or lets you use a cheaper model can pay for itself several times over, and none of those effects appear above. Use this to size the downside, then measure the upside separately — and if the added tokens are identical on every call, check what caching does to them before you argue the change is too expensive.