AI Token Counter
Paste any text and instantly see its token count for ChatGPT, Claude, and Gemini. Useful for staying inside context windows and estimating API costs.
Estimate only. Uses a heuristic of 1 token ≈ 4 chars for English text. Exact counts require OpenAI's tiktoken or Anthropic's tokenizer. Non-English text and code can consume 2-3x more tokens.
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What is a token?
A token is the unit AI models read and write. It is not exactly a word - A token can be a full word, part of a word, punctuation, whitespace, a number, or a code symbol. That is why a short paragraph, a spreadsheet export, and a JavaScript file with the same character count can all use different numbers of tokens.
Read the full definition in our glossary entry on tokens, or learn how context windows work.
Why token counts matter before you send a prompt
Every AI model has a context window - The total text it can consider at once, including your prompt, pasted documents, previous messages, and the answer it generates. If your input is too long, the model may ignore the end of the document, shorten its answer, or lose important context from earlier in the conversation.
Counting tokens before you paste a long document helps you decide whether to summarize first, split into smaller chunks, or remove irrelevant sections. This matters for legal drafts, research papers, code files, transcripts, email threads, and any workflow where missing a section can change the answer.
Check whether an article, PDF excerpt, or essay draft fits into one prompt before asking for a summary or critique.
Code, logs, JSON, and SQL consume tokens quickly. Count first before sending a full file to a coding model or the Text to SQL Generator.
Estimate prompt size before sending product pages, campaign briefs, keyword lists, or competitor copy to an AI writing tool.
Use token counts to keep internal prompt templates compact, predictable, and easier to reuse across models.
Token planning by content type
These estimates show why token counting is more than a technical detail. The larger the input, the more important it is to plan what the model should read.
| Content type | Typical size | What to do before prompting |
|---|---|---|
| Short email | 150–300 tokens | Safe to paste directly. Ask for tone, clarity, or a rewrite. |
| Blog article | 1,000–3,000 tokens | Paste and ask for a summary, outline, or SEO rewrite. |
| Meeting transcript | 5,000–20,000 tokens | Split by topic or time block if the transcript is long. |
| Code file | 2,000–15,000 tokens | Include the relevant function and surrounding context, not the entire project. |
| Legal document | 10,000+ tokens | Break into clauses or sections, then ask targeted questions about each. |
How to reduce tokens without losing meaning
- Remove headers, footers, page numbers, navigation text, and repeated disclaimers from pasted documents.
- Replace long examples with one representative example unless the model needs every case.
- Summarize background information first, then ask the model to work from the summary.
- Split large documents by section and ask focused questions instead of one broad request.
- Use clear, precise instructions. A short prompt with a precise goal often outperforms a long prompt with vague requirements.
Related tools
After counting tokens, use the right tool for the next step in your workflow.