Token Counter

A token counter is a tool that estimates how many tokens a piece of text contains for a given language model. Tokens are the basic units that LLMs use to read and generate text — they are not the same as words or characters. A single English word may be one token, but a long or uncommon word can be split into two or more tokens. Punctuation marks and spaces are also counted as tokens. Our token counter onlineprovides instant estimates by applying each model's published average characters-per-token ratio directly in your browser, with no server calls required. It also counts words, characters, sentences, and lines, making it a complete text analysis tool for prompt engineers, writers, and developers.

1. Paste or Upload Text: Type or paste any text into the input area, or upload a plain-text file (.txt, .md, .json, .py, etc.). The token counter begins analyzing immediately as you type.
2. Select a Model:Choose from the model dropdown to apply that model's tokenizer ratio. Each LLM family uses a slightly different average — OpenAI models use ~3.8 characters per token, while Google and Mistral models average ~4.0. The token count, input cost, and context usage all update instantly when you switch models.
3. Read the Results:The primary stats grid shows your token estimate, word count, character count, sentence count, and line count simultaneously. The context window bar shows how much of the selected model's context limit your text fills, and the comparison table displays token counts and input costs across all supported models at once.

• Tokens:Estimated by dividing character count by the model's average characters-per-token ratio. Reflects approximate billable API units for the selected provider.
• Words: Space-delimited word count using whitespace splitting. Useful for SEO targets, content briefs, and academic word limits.
• Characters: Total UTF-8 character count including spaces, and a separate count excluding spaces. Relevant for SMS limits, Twitter/X posts, and meta descriptions.
• Sentences & Lines: Sentence count based on terminal punctuation detection. Line count based on newline characters — useful for code files and structured documents.

This token counter uses ratio-based estimation rather than running a full tokenizer, which means estimates are accurate to within ±5–10% for typical English prose. Accuracy varies by content type: English prose and common code tokenize close to the estimated ratio, while non-English languages (especially Chinese, Japanese, Arabic) tokenize at significantly different rates — often 1–2 characters per token. Special characters, emojis, and rare Unicode glyphs also produce higher token counts than the ratio predicts. For exact billing counts, use the provider's official tokenizer: OpenAI's tiktoken, Anthropic's token counting API endpoint, or Google's countTokens API method. This tool is ideal for quick estimates and multi-model comparisons without needing to install any libraries.