LLM API cost calculator
Work out what an LLM feature actually costs per month across the major models, including prompt caching and batch discounts. Prices are editable, because they change constantly.
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Monthly cost by model
Indicative list prices, August 2026. Editable — verify with the provider.
| Model | In$/1M | Out$/1M | Per month | Per year |
|---|---|---|---|---|
| Claude Haiku 4.5Anthropic | — | — | ||
| Claude Sonnet 5Anthropic | — | — | ||
| Claude Opus 5Anthropic | — | — | ||
| GPT-5.6 LunaOpenAI | — | — | ||
| GPT-5.6 TerraOpenAI | — | — | ||
| GPT-5.6 SolOpenAI | — | — | ||
| Gemini 3 FlashGoogle | — | — | ||
| Gemini 3.1 ProGoogle | — | — | ||
| Gemini 2.5 FlashGoogle | — | — |
Cheapest option is highlighted. Cost per request is shown on hover in the monthly column title attribute.
Reading these numbers well
The cheapest model is often the right one. Most production workloads are classification, extraction or routing, where a small model performs indistinguishably from a frontier one at a fraction of the price. Route the hard cases up rather than sending everything to the largest model.
Input tokens are usually the bill. Long system prompts and retrieved context are sent on every single call. Prompt caching typically removes a large share of that, which is why the cache field above tends to move the number more than switching models does.
Conversations grow quadratically. If you resend the full history each turn, a ten-turn conversation costs far more than ten times a single turn. Summarising or truncating history is often the single biggest saving available.
This excludes everything except tokens. No infrastructure, vector storage, egress, or the engineering time to build and operate it. Inference is rarely the largest line in a real AI budget.
Costs looking uncomfortable?
Most production LLM bills can be cut substantially without changing models — caching, batching, routing simple requests to smaller models, and not sending the whole conversation every turn.
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