AI Token Cost Calculator

Estimate monthly and annual AI API inference costs from prompt tokens, completion tokens, request volume, and per-token pricing.

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Monthly cost

$71.50

Requests per month 22.0K
Input tokens per month 11.00M
Output tokens per month 4.40M
Total tokens per month 15.40M
Input cost / month $27.50
Output cost / month $44.00
Annual cost $858.00
Cost per request $0.0032
Cost per user / month $0.72

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AI API Costs

AI API token usage, monthly inference cost, and cost per request

An AI token cost calculator estimates how much an AI API integration costs per month based on prompt length, completion length, request volume, and per-token pricing. It breaks down monthly spend by input and output tokens separately, and shows cost per request and per user to help budget AI-powered features.

How AI API pricing works

Most large language model APIs price separately for input tokens (the prompt you send) and output tokens (the text the model generates). Input tokens are usually cheaper than output tokens because generating text requires more compute than reading it. Prices are typically listed per 1 000 or per 1 000 000 tokens depending on the provider.

A token is roughly four characters of English text, so 1 000 tokens is about 750 words. A short user question might use 50–100 tokens, while a complex prompt with context and instructions can use several thousand. Completion length depends on how verbose the model response needs to be.

Monthly requests = Requests/user/day × Users × Working days/month

Total API calls per month driven by user activity.

Input cost = (Prompt tokens × Monthly requests / 1000) × Price per 1K input

Total cost of tokens sent to the model each month.

Output cost = (Completion tokens × Monthly requests / 1000) × Price per 1K output

Total cost of tokens generated by the model each month.

Controlling and reducing token costs

Token costs scale linearly with volume, so the most effective levers are prompt length, completion length, and request frequency. Caching repeated system prompts, truncating conversation history, and streaming partial responses to reduce wasted completions are all common optimisations.

Model selection also has a large impact. Smaller, faster models cost a fraction of frontier models for tasks that do not require maximum capability. A tiered approach — routing simpler requests to cheaper models — can reduce average cost per request by 60–80% on mixed workloads.

Frequently asked questions

What is a token in the context of AI APIs?

A token is a chunk of text as the model processes it — roughly 4 characters or 0.75 words in English. Common words are usually one token; rarer or longer words may be two or three. Code, non-Latin scripts, and whitespace tokenise differently. Most API documentation links to a tokeniser tool so you can check exact counts for your inputs.

 Why are output tokens more expensive than input tokens?

Generating output requires the model to run the full forward pass for every token it produces, one token at a time. Reading input requires only one forward pass for the entire prompt. Because output generation is more compute-intensive and slower, providers typically charge two to four times more per output token than per input token.

 How do I find the per-token price for a specific model?

Check the pricing page of the API provider you are using. Prices change as providers optimise their infrastructure. For budgeting, use the current listed price and add a 10–20% buffer to account for price updates and model version changes over the budget period.

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