Count tokens in prompts, documents, code, and multilingual text before using the content with an AI model. Paste text or upload a TXT or Markdown file to review token, word, and character counts.
An AI token calculator estimates how text is divided into tokens, the smaller text units processed by language models. Token counts can differ from word and character counts, especially for code, punctuation, and multilingual content.
A token can be a whole word, part of a word, punctuation, or another text unit. This is why word count and token count are not the same.
Code, numbers, symbols, and different writing systems may be divided differently from ordinary English prose.
Paste text, enter it directly, or upload a supported text file, then request a calculation from the Unfox service.
Use the result as a planning reference. The exact count used by a specific model or API may differ when its tokenizer is different.
The result shows token count alongside word and character totals so you can compare several measures of input length.
This is useful when shortening prompts, reviewing document size, or preparing text for systems with token-based limits.
Token counting helps you inspect prompt and document length before using text in an LLM workflow. It is useful for editing, planning, and comparing versions of the same input.
Count tokens in a prompt, system message, code snippet, JSON payload, or conversation history.
Use the result to identify instructions or repeated context that may be longer than necessary.
Review the size of articles, research notes, documentation, and other text before adding them to an AI workflow.
When an input is too large, split it into meaningful sections and calculate each section separately.
Calculate an original prompt and a shorter revision to see how editing changes the token count.
This provides a concrete way to compare prompt length without relying only on words or characters.
Check code, punctuation-heavy text, and multilingual content, where word count alone may be a poor guide to token usage.
Remember that a model-specific API may return a different count when it uses another tokenizer.
Token counts are useful wherever prompts, documents, code, or conversation history are prepared for language-model workflows.
Inspect prompts, system messages, code, JSON, and conversation history before sending them to an AI service.
Compare the length of common request templates and review how changes affect token usage.
Use token counts as a planning input when preparing documents and workflows for AI tools.
Count tokens in articles, notes, documents, and everyday prompts before using them with an LLM.
Token calculation is only one part of working with AI content. Explore more Unfox tools to check text, refine AI-assisted writing, and analyze AI-generated images and audio.
Check text for patterns associated with AI-generated writing and review the results before publishing, submitting, or editing your content.
Check AI ContentRefine AI-generated text for clearer, more natural writing while preserving the original meaning and key information.
Humanize AI TextAnalyze images for signals that may indicate AI generation and get an additional layer of insight when reviewing visual content.
Check an ImageAnalyze audio for signals associated with AI-generated or synthetic voices to help you review questionable voice content.
Check AI VoiceText is sent to the Unfox calculation service to return a token count. Remove passwords, API keys, personal information, and other confidential material before submitting content, and review the Privacy Policy for current data-handling details. Learn more about how we handle data in our Privacy Policy.
Review token, word, and character counts in one result.
Paste content directly or upload a supported plain-text or Markdown file.
The interface counts Chinese, Japanese, and Korean characters when displaying words.
Submit text in the browser and receive a token count from the calculation service.
Quick answers about AI tokens, token counting, words, characters, multilingual text, and model-specific differences.
For typical English prose, 1,000 words is often estimated at roughly 1,300 to 1,500 tokens, but this is only a general guide. The actual count depends on the tokenizer, language, punctuation, formatting, and content type.
A token is a unit of text processed by a language model. It may be a whole word, part of a word, punctuation, a number, or another text fragment, so token count is not the same as word count.
Characters are individual letters, symbols, and spaces; words are human-readable units; tokens are pieces created by a tokenizer. One word may be one token or several tokens depending on the text and tokenizer.
Different model families may use different tokenizers and vocabularies. The same prompt, code snippet, or multilingual text can therefore produce different counts. Treat the Unfox result as a planning reference unless you verify it with the exact model tokenizer.
Languages and writing systems can be divided into tokens differently. English, Chinese, Japanese, Arabic, and other languages may have different relationships between tokens, words, and characters under a given tokenizer.
A context window is the amount of information a language model can process within a request, usually measured in tokens. System instructions, prompts, conversation history, documents, and generated output may all use part of that limit.
Yes. You can use the Unfox AI Token Calculator to count tokens in text and supported text files. The result also shows word and character totals for additional context.
Text is sent to the Unfox calculation service to return a token count. Remove passwords, API keys, personal information, and confidential material before submitting content, and review the current Privacy Policy for data-handling details.
Use the AI Token Calculator to review token, word, and character counts for prompts, code, and documents.
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