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The Token Cost Calculator converts text into tokens and calculates the exact API cost for any LLM provider. It helps developers understand the relationship between text length, token count, and cost across different models and pricing tiers.

Formule

Cost = (Token Count / 1,000,000) × Price per Million Tokens

W: Word Count (words) — Number of words in the text
T: Token Count (tokens) — Number of tokens after tokenization
P: Price per Million ($/1M tokens) — Model-specific per-million-token rate
C: Cost ($) — Total cost for the given token count

Guide étape par étape

1Paste your text or enter a word/character count to estimate tokens
2Select the LLM provider and model for pricing
3Specify whether the text is input (prompt) or output (completion)
4View the token count, cost for this text, and cost per word

Exemples résolus

Entrée

1,000 words of English text as input to GPT-4o

Résultat

Approximately 1,333 tokens. Cost: 1,333 / 1,000,000 × $2.50 = $0.0033 (one-third of a cent).

Entrée

10,000 words of output from Claude 3.5 Sonnet

Résultat

Approximately 13,333 tokens. Cost: 13,333 / 1,000,000 × $15.00 = $0.20 (twenty cents).

Erreurs courantes à éviter

✕Assuming 1 word = 1 token — in English, 1 token averages about 0.75 words or 4 characters
✕Forgetting that code, non-English text, and special characters often tokenize into more tokens per word
✕Not separating input and output costs when budgeting — they have very different rates

Questions fréquentes

How many tokens is a typical ChatGPT conversation?

A typical conversation turn is 50-200 tokens for the user message and 100-500 tokens for the response. A 10-turn conversation accumulates approximately 2,000-5,000 total tokens. System prompts add 100-2,000 additional tokens per call depending on complexity.

Do different languages use different numbers of tokens?

Yes, significantly. English is most efficiently tokenized (~1.3 tokens per word). Chinese, Japanese, and Korean use 2-3 tokens per character. Arabic, Hindi, and other scripts may use 3-5 tokens per word. This means non-English API calls can be 2-4x more expensive per equivalent meaning.

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