EN

Token Counter for AI Prompts

Paste your prompt or text and see an estimate of the token count with a realistic range. The figures are based on measurements with OpenAI tokenizers for the selected text language. Your text stays in the browser.

Paste with Ctrl+V. Counted live as you type.

Your text stays in your browser – it is not sent, not stored and not added to the address bar.

German needs about 15% more tokens than English for the same number of characters.

Result

Estimated tokens
≈ 71
Typical range
61 to 83 tokens
Characters
276
Characters without spaces
230
Words
46
Lines
3
An estimate, not an exact count: every AI model splits text with its own tokenizer. The range covered 80–90% of our test texts (OpenAI o200k_base and cl100k_base).

How it is calculated

Tokens are the units a language model breaks text into – whole words, parts of words, punctuation, or spaces. Billing and limits (context window, maximum reply length) are measured in tokens, not characters.

Rule of thumb: about 4 characters per token – in English

OpenAI says its tiktoken tokenizer averages about 4 bytes per token; Google states “about 4 characters” per token for Gemini. Both figures are for English text. Other languages need more tokens, because tokenizers are trained mostly on English text and words like “Stadtbibliothek” (German for “city library”) break into several pieces.

Where the numbers come from

We measured the OpenAI tokenizers o200k_base (GPT-4o and newer) and cl100k_base (GPT-4, GPT-3.5) on 39 to 56 plain-text samples per language (as of September 27, 2026). Average characters per token:

The estimate divides the character count by the average of both tokenizers; the range uses the 10th and 90th percentile of the individual texts. Program code, tables, lots of numbers, emoji, or languages with other scripts (Chinese, Arabic, …) can deviate significantly.

Why not an exact counter?

An exact tokenizer would need to load the full token table – for o200k_base that’s about 3.6 MB. That would be disproportionate for a fast page. Claude, Gemini, and Llama also use their own tokenizers; only the respective provider can give exact numbers (for example via a token-counting endpoint).

Frequently asked questions

How many tokens are 1,000 words?

In English, roughly 1,300–1,400 tokens (measured average: 1.4 tokens per word). German needs more: depending on the tokenizer, about 1.8–2.1 tokens per word, so roughly 1,800–2,100 tokens.

Is the count accurate for ChatGPT?

No, it’s an estimate based on measurements with the ChatGPT tokenizers. In our test texts, 80–90% of the real values fell within the shown range; the midpoint was off by a median of 6–12%.

Does this also work for Claude or Gemini?

Roughly, yes, but every model has its own tokenizer. Google also states about 4 characters per token for Gemini in English. Exact numbers are only available through each provider’s own interface, such as an OpenAI, Claude, or Gemini token-counting endpoint.

Is my text uploaded anywhere?

No. Counting happens only in your browser; the text is never sent, never stored, and never written to the address bar.

Why are spaces and line breaks counted too?

Tokenizers also process whitespace: a space is usually attached to the following word, and multiple spaces or line breaks become their own tokens. That’s why the estimate is based on every character.

Sources and legal basis

As of:

Related tools