Tokens ↔ Words ↔ Characters Converter
Convert between tokens, words and characters for LLM prompts. Type any one and get the other two instantly. Free, runs in your browser.
Tokens, Words and Characters Converter
Type any one value — the other two update instantly. Based on the English average of 1 token ≈ 0.75 words ≈ 4 characters. For an exact GPT count, use the Token Counter.
How to Use the Converter
Type into any box
Enter a number of tokens, words, or characters. You only ever fill one — the tool works out the other two for you.
Read the conversion
The other two boxes update instantly using the English average of about 0.75 words and 4 characters per token.
Use it to size a prompt
Heading into a model with a token budget or a context window? Convert your word count to tokens to see roughly how much room you'll use.
Switch to exact when it matters
These are averages. For billing or a tight context window, count the real text with the Token Counter, which runs the actual GPT tokenizer.
Tokens, Words and Characters: A Quick Mental Model
Why a rough conversion is so useful
You usually think in words — "a 500-word email", "a 2,000-word article" — but language models think in tokens, and that's the unit of both their context limit and their price. A fast conversion between the two lets you answer everyday questions without pasting anything into a tokenizer: will this article fit in the prompt? Roughly how much will this batch cost? Is my system prompt eating too much of the window? OpenAI's published rule of thumb is that English runs about 0.75 words per token, or equivalently about 4 characters per token — so 1,000 tokens is roughly 750 words. Other vendors quote different averages (Anthropic puts a Claude token at roughly 3.5 English characters), so this converter sits at the OpenAI end of the range.
That ratio is an average, not a law. Tokenizers split common words into single tokens but break rare or long words into pieces, and they count spaces and punctuation. Code, URLs, numbers and non-English text all tokenise less efficiently — often noticeably more tokens per word than prose. So treat this converter as a planning instrument: perfect for a quick sense of scale, but reach for an exact tokenizer when a few percent matters for your bill or your context budget.
"Think in words, budget in tokens. A good estimate bridges the two in your head — an exact count settles the invoice."
Where the estimate drifts
The 4-characters-per-token figure holds up well for ordinary English writing. It starts to drift in predictable directions: dense code and JSON push the token count up relative to characters because of symbols and indentation; long technical terms and identifiers split into multiple tokens; and languages with non-Latin scripts can use dramatically more tokens per character. If your text is mostly one of those, nudge your mental ratio accordingly — or just measure it. The point of this tool is to get you 90% of the way there in a second, so you only spend the effort of an exact count when the last few percent actually changes a decision.
Code and JSON burn more tokens per character than prose
English averages about 0.75 words per token — so 1,000 tokens ≈ 750 words.
Equivalently, English runs about 4 characters per token on average.
A token is often a whole common word, but rare or long words split into several tokens.
Spaces and punctuation are tokens too — and a leading space changes a word's token.
Code and JSON use more tokens per character than prose because of symbols and indentation.
Non-Latin scripts often use far more tokens per character than English.
Context windows and API prices are both measured in tokens, not words.
These conversions are estimates — the only exact count comes from running a tokenizer.
A typical page of text (~500 words) is roughly 650–700 tokens.
This tool runs entirely in your browser — nothing is sent anywhere.
Frequently Asked Questions
- About 750 words of English, since the average is roughly 0.75 words per token (or about 4 characters per token). It varies with punctuation, formatting and language, so treat it as a close estimate rather than an exact figure.
- Roughly 650–700 tokens for typical English prose (500 ÷ 0.75 ≈ 667). Dense code, long technical terms or non-English text will push that number higher.
- No — it's an average-based estimate. The only way to get an exact count is to run the model's tokenizer on the actual text. For OpenAI models you can do that with our Token Counter, which uses the real tiktoken encodings.
- Tokenizers are trained mostly on English text, so common English words map to single tokens. Symbols, indentation, long identifiers and non-Latin scripts don't fit those patterns and get split into more tokens, raising the token-per-character ratio.
- Yes, by enough to matter for a budget. Different model families use different tokenizers: OpenAI's published average is about 4 characters per token, while Anthropic's glossary puts a Claude token at about 3.5 characters — roughly 14% more tokens for the same English text. This converter uses the OpenAI ratio; for a precise per-model figure you need that model's tokenizer.
- Because you often only know a word count — "a 2,000-word article" — and want a quick sense of whether it fits a context window or how much it might cost, without pasting the text anywhere. This converter gives that instant ballpark; the Token Counter gives the exact figure when you have the text.
- Yes. Type into tokens, words or characters and the other two update. It's fully bidirectional, so you can start from whichever number you already have.
- No. It's just arithmetic running in your browser. The numbers you type are never sent to any server or third party, and nothing is stored.
- This tool only converts counts. To turn token counts into a dollar figure, use our LLM Cost Calculator, which applies each model's input and output rates to your token numbers.
- It is OpenAI's published rule of thumb for English text — about 4 characters or ¾ of a word per token, so 100 tokens is roughly 75 words. Other vendors publish different averages: Anthropic, for example, puts a Claude token at roughly 3.5 English characters, which would make the same text about 14% more tokens than this tool shows. Treat the figure as a planning estimate at the OpenAI end of the range, and use the tokenizer of the model you are actually billed on when it matters.
Related News
You may be interested in these recent stories from our newsroom.
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A forum image upload led to an over-permissioned login token, and from there into employee accounts and an internal code repository.
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Google Gemini Breached Three Real Companies During a Security Test
A capture-the-flag exercise handed a model internet access it was never meant to have, aimed at a company that turned out to exist.
Method & sources
How it computes
Bidirectional estimate between tokens, words and characters using OpenAI's published English averages: 1 token ≈ 0.75 words ≈ 4 characters (so words = tokens × 0.75, characters = tokens × 4, and the inverses). Pure arithmetic in the browser — no tokenizer runs and no text is read.
What this tool implements
- OpenAI rule of thumb for English text: 1 token ≈ 4 characters ≈ ¾ word; 100 tokens ≈ 75 words.
- Results are estimates for planning; exact counts require the billed model's tokenizer (tiktoken for OpenAI models).
- Vendor ratios differ — Anthropic states ≈ 3.5 English characters per Claude token — and the page says which end of the range it sits on.
- Inputs are numbers only; no text is tokenised and nothing leaves the browser.
Sources
- OpenAI Help Center. What are tokens and how to count them? https://help.openai.com/en/articles/4936856-what-are-tokens-and-h…
- Anthropic. Claude Developer Platform glossary — Tokens: https://platform.claude.com/docs/en/about-claude/glossary
What can make this go out of date
- none at runtime
Pick up where you left off
Stored only in this browser — never sent to our servers.