Everyone reaches for the most expensive model "just to be safe". Result: API costs multiply, and ~80% of tasks never needed it. The real math is simpler — and cheaper — than it looks.
When you build on LLM APIs, price per million tokens is the number that matters. But comparing official price lists is a trap — the real cost depends on how you use them.
How to read pricing tables:
- Input vs output: generating text costs 3–5x more than reading it;
- Long context: bigger context in every call burns tokens fast;
- Caching: repeated context can be heavily discounted;
-
Small models: for ~80% of tasks a
mini/flashmodel is enough and costs far less (10–30x on OpenAI, varies by provider).
Practical rule of thumb:
| Task | Model |
|---|---|
| Classification, extraction, formatting | Small model |
| Complex code & reasoning | Claude Sonnet / top-tier GPT |
| Huge context (docs, codebases) | Gemini / Claude |
| Simple translation & summarization | Any small model |
What most people miss: real cost is price × tokens used. A 3x more expensive model that makes fewer mistakes can end up cheaper — no retries, no manual fixes.
Want to try these models in your project? **ModelKiwi* gives you access to GPT, Claude and Gemini with PIX payment (no international credit card needed) and free credits to start: https://www.modelkiwi.com. WhatsApp: +5521999500402 — and join our channel: https://t.me/ModelkiwiOfficial.*
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