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LLM Token Cost Calculator

Computes the cost of large-language-model API calls from the number of input, cached-input and output tokens per request, the prices per million tokens supplied by the caller, and the number of requests.

When to use

You are budgeting an LLM feature, comparing providers at their published per-million-token prices, or estimating the monthly bill for a given request volume.

Do not use when: You need token counts from text (tokenizers are model-specific; roughly 4 characters per token in English), or provider-specific extras such as batch discounts, image tokens or fine-tuning fees.

Formula

cost_per_request = (input_tokens − cached_input_tokens) × p_in / 10⁶ + cached_input_tokens × p_cached / 10⁶ + output_tokens × p_out / 10⁶; total_cost = cost_per_request × requests

Pure multiplication with caller-supplied prices in any currency; no price list is built in, so check the provider's current published rates. Cached tokens must not exceed input tokens.

Inputs

Parameter	Type	Unit	Required	Description
`input_tokens`	integer	tokens	yes	Prompt tokens per request, including any cached portion. Range: ≥ 0, ≤ 1000000000
`output_tokens`	integer	tokens	yes	Completion tokens per request. Range: ≥ 0, ≤ 1000000000
`price_input_per_million`	number	per 1M tokens	yes	Price per million uncached input tokens, in your currency. Range: ≥ 0, ≤ 100000
`price_output_per_million`	number	per 1M tokens	yes	Price per million output tokens. Range: ≥ 0, ≤ 100000
`requests`	integer		default 1	Number of requests (e.g. per month). Range: ≥ 1, ≤ 1000000000000
`cached_input_tokens`	integer	tokens	default 0	Part of input_tokens served from a prompt cache and billed at price_cached_per_million. Range: ≥ 0, ≤ 1000000000
`price_cached_per_million`	number	per 1M tokens	default 0	Price per million cached input tokens (often 10–50 % of the input price). Range: ≥ 0, ≤ 100000

Outputs

Output	Type	Unit	Description
`input_cost`	number		(input_tokens − cached_input_tokens) × price_input_per_million / 10⁶.
`cached_cost`	number		cached_input_tokens × price_cached_per_million / 10⁶.
`output_cost`	number		output_tokens × price_output_per_million / 10⁶.
`cost_per_request`	number		Sum of the three per-request components.
`total_cost`	number		cost_per_request × requests.
`cost_per_1000_requests`	number		cost_per_request × 1000.
`tokens_per_request`	integer	tokens	input_tokens + output_tokens.
`total_tokens`	integer	tokens	tokens_per_request × requests.
`blended_price_per_million`	number	per 1M tokens	total_cost / total_tokens × 10⁶: the effective price per million tokens of the mix.
`output_share_percent`	number	%	Output cost as a percentage of the cost per request.

Example

1,000 in / 500 out at 3 and 15 per million, one request: {"input_tokens":1000,"output_tokens":500,"price_input_per_million":3,"price_output_per_million":15,"requests":1} → {"input_cost":0.003,"output_cost":0.0075,"cost_per_request":0.0105,"total_cost":0.01,"tokens_per_request":1500,"output_share_percent":71.43}

Same request 10,000 times: {"input_tokens":1000,"output_tokens":500,"price_input_per_million":3,"price_output_per_million":15,"requests":10000} → {"total_cost":105,"cost_per_1000_requests":10.5,"total_tokens":15000000,"blended_price_per_million":7}

GET https://tttkmbb.com/api/v1/calculate/llm-token-cost?input_tokens=1000&output_tokens=500&price_input_per_million=3&price_output_per_million=15&requests=1

Machine access

API: GET https://tttkmbb.com/api/v1/calculate/llm-token-cost (query parameters) or POST with a JSON body {"inputs": {...}}
Schema: https://tttkmbb.com/api/v1/calculators/llm-token-cost · Markdown: https://tttkmbb.com/developer/llm-token-cost.md · JSON definition: https://tttkmbb.com/developer/llm-token-cost.json
MCP: server https://tttkmbb.com/mcp, tool run_calculator with calculator_id="llm-token-cost"
OpenAPI operationId: calculate_llm_token_cost
Freshness: static. Authentication: none. Rate limit: fair use (see rate limits).

Sources

OpenAI Help Center – What are tokens and how to count them? (reference)
Byte pair encoding (Wikipedia) (reference)

FAQ

How do I count tokens?

Use the provider's tokenizer (tiktoken for OpenAI models, the Anthropic token-count endpoint); as a rule of thumb one token is about 4 English characters or ¾ of a word.

How is prompt caching modelled?

cached_input_tokens is the part of each request's prompt billed at the cheaper cached rate; the remainder pays the full input price. Cache-write surcharges are not modelled.

Why are output tokens so much more expensive?

Generation is sequential (one forward pass per token) while prompt tokens are processed in parallel, so providers price output 3–5× higher than input.

Related calculators

LLM VRAM Calculator — Memory needed to self-host instead of paying per token.
Percentage Calculator — Percent arithmetic for discounts and shares.
Reading Time Calculator — Words (≈ tokens × 0.75) to reading time.