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Free Token Counter for LLMs
Paste text to estimate how many tokens it uses and how much of a model's context window it fills. Counting happens in your browser; nothing is sent anywhere.
≈ Tokens
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Words
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Characters
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Lines
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Context window fit
What a token is
A token is the unit a language model reads and writes: a common word, part of a longer word, a punctuation mark or a space-plus-word. Models are priced and limited in tokens, not characters. As a rule of thumb from OpenAI's help center, one token is about four characters of English, or roughly three-quarters of a word.
This counter uses that ratio, blended with a word-based estimate and adjusted for non-Latin characters, which usually take more tokens each. It's an estimate: exact counts differ between tokenizers, and every model family has its own. For billing-accurate counts, use the provider's tokenizer or token-counting API.
Why it matters for AI visibility
When an AI engine searches the web, it doesn't read your whole page into the answer. It retrieves passages and fits them into a limited budget alongside other sources. Long, padded pages spread the important facts thin. Short, self-contained sections — a question, then a direct answer in a few sentences — survive that squeeze.
Use the counter to check a section's length, a prompt's size or an llms-full.txt file. To measure a live page, the AI crawler view estimates tokens for the content a crawler actually gets.
Reading the context bars
The bars show how much of common context-window sizes — 8K, 32K, 128K, 200K and 1M tokens — your text would take. Real limits include the system prompt, the conversation so far and room for the reply, so plan to use well under 100%.
FAQ
Questions
Is this token count exact?+
No. It's an estimate based on the common four-characters-per-token rule for English. Each model's tokenizer splits text differently, so expect differences of several percent — more for code and non-English text.
Is my text sent to a server?+
No. The counting runs entirely in your browser. Nothing you paste leaves the page.
Why do non-English texts use more tokens?+
Tokenizers are trained mostly on English, so English words often map to one token while words in other languages or scripts are split into several pieces.
Keep reading
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