Token Counter

The unit language models read and bill by. A tokenizer cuts text into common fragments: a frequent English word is often one token, a rare word several, and punctuation, digits and spaces count too. On typical English prose, one token is about three quarters of a word; code, numbers and many non-Latin scripts use more tokens per word.

The two OpenAI counts use OpenAI’s published tokenizers — o200k_base (GPT-4o, GPT-4.1 and the o-series) and cl100k_base (GPT-4, GPT-3.5 and the text-embedding-3 models) — so they are exact for the text itself. An API request adds a few tokens of message formatting on top.

Anthropic and Google do not publish tokenizers that can run offline in a browser; exact counts come from their APIs, which would mean sending your text to them. The range shown (0.9× to 1.35× the o200k count) covers typical differences between modern tokenizers — treat it as a planning figure, not a bill.

Yes. Drop the PDF and its text is extracted exactly as PDF to Markdown would do it — with OCR for scanned pages — and then counted. Word, HTML, Markdown, CSV, JSON and plain-text files work too.

No. The tokenizer is downloaded from this site once (about 2 MB, then cached) and runs in your browser, in a background thread so typing stays smooth. Your text never leaves your device.

Split it. The Markdown Chunker cuts text into pieces of a set token size at headings and paragraphs; Split PDF for AI cuts a PDF into parts that fit ChatGPT, Claude, NotebookLM or Gemini upload limits.