How to Count AI Tokens

Count tokens for GPT, Claude, and other LLM APIs instantly. Estimate API costs, check context window limits, and optimize your prompts with our free token counter.

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Step-by-Step Guide

1

Paste your text or prompt

Enter or paste the text you want to tokenize into the input area. This can be a prompt, system message, API request body, or any text content. The tool handles multi-line text, code blocks, and special characters accurately.

2

Select the model or tokenizer

Choose the target model to get accurate token counts — different models use different tokenizers. GPT-4 and GPT-3.5 use cl100k_base, while Claude uses its own tokenizer. The token count can vary significantly between models for the same text.

3

View the token breakdown

See the total token count along with a visual breakdown of how your text is split into tokens. This helps you understand why some words use more tokens than others — for example, uncommon words often get split into multiple tokens.

4

Estimate API costs

Based on the token count and selected model, the tool calculates the estimated API cost for input and output tokens. Use this to budget your API usage, optimize prompt length, and ensure you stay within context window limits.

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Frequently Asked Questions

What exactly is a token?
A token is a chunk of text that language models process as a single unit. It can be a whole word, part of a word, or even a single character. On average, one token equals about 4 characters or 0.75 words in English, but this varies by language and content type.
Why do token counts differ between models?
Different AI models use different tokenization algorithms (called tokenizers). GPT-4 uses cl100k_base which has a 100K token vocabulary, while other models may split text differently. The same sentence can produce different token counts depending on which tokenizer processes it.
How do tokens affect API cost?
API providers charge per token for both input (your prompt) and output (the model response). Knowing your token count helps you estimate costs before making API calls. For example, GPT-4 charges differently for input vs output tokens, so optimizing prompt length directly reduces your bill.
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