Calculators
LLM API Token Cost Estimator
Estimate monthly large language model API costs from requests, input and output tokens, cache hits and per-million prices.
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The tool
Enter your numbers below. Results update as you type, and nothing leaves your browser.
Everything you enter stays on your own machine. Nothing is sent to us or to anyone else, which you can confirm in your browser network tab.
What people use it for
- Budgeting a chatbot or AI assistant
- Comparing models with different prices
- Pricing an AI feature for customers
- Estimating savings from prompt caching
About this tool
What the LLM API Token Cost Estimator does
AI model APIs charge per token, with separate prices for input and output and often a discount for cached input. This calculator multiplies your request volume by tokens per request and the price per million tokens, so you can budget an AI feature before launch.
Formula
- Requests per month = requests per day × days
- Cost = uncached input tokens ÷ 1M × input price + cached tokens ÷ 1M × cache price + output tokens ÷ 1M × output price
Worked example
Using the starting values in the calculator above:
| Input | Value |
|---|---|
| Requests per day | 5,000 |
| Input tokens per request | 1,200 |
| Output tokens per request | 400 |
| Input price per million tokens | $3.00 |
| Output price per million tokens | $15.00 |
| Input served from cache | 0% |
| Cached input price per million | $0.30 |
| Days per month | 30 |
| Result | Value |
|---|---|
| Monthly API cost | $1,440 |
| Cost per 1,000 requests | $9.60 |
| Cost per request | $0.01 |
| Tokens per month | 240,000,000 |
| Share of cost from output | 62.5% |
| Annual cost | $17,280 |
How to read the result
Output tokens usually cost several times more than input, so asking for shorter, structured answers is often the cheapest optimisation.
Long system prompts and retrieved documents are input tokens on every request. Prompt caching can cut that cost sharply when the same context repeats.
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Questions
Questions about this tool
The things clients actually want to know — costs, timelines, ownership and what happens after launch.
In English, a token is roughly three quarters of a word on average, so 1,000 words is around 1,300 tokens. It varies by model and language.
Generating each output token takes more computation than reading input tokens.
A feature where repeated input, such as a long system prompt, is stored and billed at a reduced rate on later requests.
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