What is ai token counter?
Large language models read and write tokens, which are pieces of words. Pricing, speed and context limits are all based on tokens, not words.
This counter gives a quick estimate and shows what share of common context windows your text would fill.
How to use the ai token counter
- Paste your prompt or document.
- Read the estimated token range.
- Compare it with the context windows in the table.
How the ai token counter works
Two rules of thumb are averaged: characters divided by 4, and words times 1.33. Each model uses its own tokenizer, so exact numbers differ.
Worked example
Common uses of the ai token counter
- Checking whether a long document fits a model's context window.
- Estimating the cost of a prompt before sending it.
- Trimming prompts to save money and speed.
- Comparing how efficient different languages are.
- Planning chunk sizes for retrieval systems.
Tips and common mistakes
Code and non English text usually use more tokens per word.
Long inputs cost more and may reduce quality. Trim anything the model does not need.
Background: what to know before you rely on it
Language models do not read letters or words directly. They split text into tokens, which can be a whole short word, part of a longer word or a punctuation mark. A common English word is often one token, while rare words, names and code are split into several. Non English text, especially languages with non Latin scripts, usually needs more tokens per word. The context window is the maximum number of tokens the model can handle in a single request, including your prompt and its answer. Long contexts cost more and can reduce answer quality, so include only what the model needs.
Private, free and fast
This tool runs entirely in your browser. What you type or choose is not uploaded to a server and is not saved after you close the page, which keeps it fast and private. It is free to use as often as you like. See more in all ai tools, or browse the full list of tools.
Frequently asked questions
What is a token?
A piece of text, often a part of a word, that the model processes.
Is the count exact?
No, it is an estimate. Use the provider's tokenizer for exact numbers.
How many tokens is 1,000 words?
About 1,300.
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