Providers price input and output tokens separately — and output usually costs 3–5× more than input. So a short prompt with a long answer can cost more than a long prompt with a short answer. Enter your two numbers and see the real per-call cost across models.
Cost per call by model
Your token counts, every model, ranked cheapest first. Prices are per 1M tokens.
| Model | In $/1M | Out $/1M | Cost / call |
|---|
How token cost works
The formula is simple: cost = (input tokens ÷ 1,000,000 × input price) + (output tokens ÷ 1,000,000 × output price). The two prices are different on purpose — generating text (output) is more expensive than reading it (input), typically by 3–5×. That's why the biggest lever on your bill is usually capping output length, not trimming the prompt. As a rough guide, 1,000 tokens ≈ 750 English words, but code and other languages tokenize differently, so measure with the provider's tokenizer for exact figures.
Next steps
Estimating a whole product, not one call? Use the AI API cost calculator or the AI app cost estimator. Building an agent or chatbot? Try the AI agent cost calculator or chatbot cost calculator. Reusing the same context every call? Prompt caching and batch jobs each cut cost further.