How to use this calculator

1. Enter your monthly request volume. The total number of API calls you expect per month. If you think in requests per second, multiply by ~2.6 million for a steady month.
2. Set average input tokens per request. Everything you send: system prompt, context/RAG payload, few-shot examples, and the user message. Long context drives this up fast.
3. Set average output tokens per request. What the model generates back. This is the expensive half, so estimate it carefully — and trim it where you can.
4. Tick the models to compare. The table re-sorts instantly, cheapest first, and flags the lowest-cost option.
5. Read the delta. The "+%" next to each model shows how much more it costs than the cheapest, so trade-offs (quality vs. price) are obvious at a glance.

Get your token numbers from the usage object every provider returns on a real sample of traffic, then average. Guessing is fine for a first pass — just refine once you have data.

Tips to reduce AI API costs

Once the calculator shows where your money goes, these levers move the bill the most — roughly in order of impact:

1. Right-size the model

The biggest savings come from not over-paying for capability you don't need. Route easy, high-volume tasks (classification, extraction, short answers) to a mini/flash-tier model and reserve flagship models for genuinely hard work. A two-tier setup often beats picking a single model for everything — see our OpenAI vs Anthropic pricing comparison for worked examples.

2. Use prompt caching

If you send the same long prefix on every call — a big system prompt, few-shot examples, a reused document — mark it cacheable. Cached input is billed at a steep discount (often 50–90% off), which is pure savings on input-heavy workloads. Output cost is unaffected.

3. Cut output length

Because output is the priciest tokens, shorter responses save the most. Ask for concise answers, set a realistic max_tokens, request structured/JSON output instead of prose where you can, and stop the model from "thinking out loud" when you only need the result.

4. Trim and compress input

Don't stuff the whole knowledge base into context. Retrieve only the relevant chunks, drop stale conversation history, and summarize long threads. Every token you don't send is a token you don't pay for — on every request, forever.

5. Batch non-realtime work

For jobs that don't need an instant response (bulk summarization, backfills, evals), batch endpoints typically apply around a 50% discount and use separate, higher rate limits — a double win.

6. Avoid silent retry waste

Retries on rate limits and timeouts cost real tokens. Cap and jitter your backoff so a blip doesn't turn into a billing spike — see the 429 rate-limit guide.