LLM API Cost Calculator
Enter your usage once — see what it costs per month on every major model. No sign-up, runs in your browser.
Monthly cost by model
| Model | Provider | Input $/1M | Output $/1M | Per request | Per day | Per month |
|---|
How this calculator works
Every LLM API bills the same way: a price per million input tokens (your prompt, system message, conversation history, retrieved documents) and a separate, higher price per million output tokens (the model's response). Your monthly bill is simply:
(input tokens × input price + output tokens × output price) × requests
The hard part is that every provider publishes prices on a different page, in a different format, with different caching rules — so comparing them honestly takes an afternoon. This table does that comparison in one place and we keep the numbers current as providers change them (which they do almost monthly).
What usually surprises people
- Output tokens dominate. Output is typically 3–5× the input price. A chatbot that writes long answers costs far more than one that writes short ones, even with identical traffic.
- Prompt caching is the biggest lever. If every request shares a long system prompt or document, cached-input pricing (often 90% off) can cut the input side of your bill dramatically. Use the "cached input share" selector above to model it.
- The spread is enormous. Between a frontier flagship and a budget model there is often a 50–100× price difference. Many production workloads run fine on mid-tier models.
- Batch APIs are the other big lever. If your workload can tolerate up to ~24h turnaround (bulk classification, offline evaluation, dataset generation), OpenAI, Anthropic and Google all offer async batch endpoints at roughly half the standard price. Toggle "Use Batch API pricing" above to see it — it doesn't stack with prompt caching in this calculator, since providers bill them as separate modes.
FAQ
How do I estimate tokens per request?
Rule of thumb for English: 1 token ≈ 4 characters ≈ 0.75 words. A typical chat message is 50–200 tokens; a page of text is ~500 tokens; a long system prompt with instructions can be 1,000–3,000 tokens. Or click "count exactly" next to the input field above and paste a real sample — it runs OpenAI's actual tokenizer in your browser (nothing is uploaded) and fills the field in for you.
Are these prices up to date?
Yes — this table syncs automatically on a schedule from a live pricing feed that tracks each provider's current per-token rate, including active promotions. That's the point: a hand-maintained comparison goes stale within weeks, this one doesn't. The "last synced" date above is the last automated run, not a one-time snapshot. Regional endpoints and batch APIs can still be cheaper — footnotes under the table flag the big ones.
Why is my real bill higher than this estimate?
The usual culprits: conversation history re-sent on every turn (input grows with each message), reasoning/thinking tokens billed as output on some models, retries, and tool-calling round trips. Multiply your naive estimate by 1.5–3× for a multi-turn assistant.
Which model should I pick?
Price this table can tell you; quality it can't. A sane default: prototype on a flagship model, then step down tier by tier until quality degrades, and stay one tier above that.
What's Batch API pricing, and should I use it?
OpenAI, Anthropic and Google all let you submit requests to a queue instead of getting an instant response — you get results within a set window (often up to 24h) at roughly half the standard price. It's a good fit for anything that isn't a live user waiting on a reply: bulk classification, offline evals, synthetic data generation, nightly report generation. Not every provider on this page offers it — models without a batch tier are marked "std rate" and show their normal price when the toggle is on.