AI Token Counter

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What Is an AI Token Counter?

An AI token counter measures how many tokens a piece of text consumes when processed by language models like GPT-4, Claude, or Gemini. Tokens are the fundamental units these models read and generate — and they directly determine API costs, context window usage, and response length limits.

This tool lets you paste or type text, pick a model, and instantly see an estimated token count against that model's context window, with example API costs. It helps developers, content creators, and AI power users optimize their prompts, manage context windows, and predict API expenses before making calls.

How to Use This Tool

  1. Paste or type your text — Enter the content you want to tokenize — a prompt (structure with our AI Prompt Template Builder), document, code snippet, or any text. The tool processes input in real time as you type.
  2. Select your model — Choose the model. The count is an estimate at 4.6 characters per token, the average we measured with OpenAI’s o200k and cl100k tokenizers on our own articles. Other providers’ tokenizers are not available in the browser, so treat their counts as approximate.
  3. Review the results — See the estimated token count, character count, word count, and estimated API cost.
  4. Optimize if needed — If your text exceeds a context window or budget, use the insights to trim content. Trim, re-check the estimate, and repeat.

Worked example: estimating one request

Paste a 2,000-word English article, about 12,000 characters. The tool divides by 4.6 characters per token, the average OpenAI's o200k and cl100k tokenizers gave on 25 of our own articles, and shows 2,609 tokens. Because individual articles ranged from 3.8 to 5.2 characters per token, the real count for this text is likely between 2,308 and 3,158. Code, URLs, numbers and non-English text use more tokens per character, so estimates for them run low.

At the built-in Claude 3.5 Sonnet example rate ($3 per million input tokens, $15 per million output tokens), sending that article costs about $0.0078, and asking for a 1,000-token summary adds $0.015, so output is the larger cost even though it is shorter. The model list carries 2024-25 example rates and context windows; providers change prices often, so check the provider's pricing page before budgeting, and use the provider's own token-counting endpoint when you need an exact number.

Characters-per-token figure measured by UDT in October 2026 with OpenAI's published tokenizers (o200k_base, cl100k_base) on 25 English articles; arithmetic as shown.

Frequently Asked Questions

What is a token in AI?+
A token is a piece of text that AI language models process as a single unit. Tokens can be whole words, parts of words, or individual characters depending on the tokenizer. For English prose, OpenAI’s current tokenizers average about 4.6 characters per token in our measurements, so 1,000 words is roughly 1,400 tokens.
Why does token count matter?+
Token count determines API costs, context window limits, and response lengths. Every AI API charges per token, and each model has a maximum context window. Knowing your token count before sending a request helps you stay within limits and manage costs.
How accurate is this tool?+
It is an estimate, not a tokenizer. The count uses 4.6 characters per token, the average OpenAI’s o200k and cl100k tokenizers gave on 25 of our English articles (October 2026); individual articles ranged from 3.8 to 5.2, so a single text can land roughly 15 to 20 percent either side. Code, URLs and non-English text vary more. For an exact count, use the provider’s own tokenizer or token-count API.
What tokenizers does it support?+
None run in the browser: the same character-based estimate is used for every model, and the model choice sets the context window and the example rates. OpenAI publishes its tokenizers (cl100k_base, o200k_base), and our 4.6 figure was measured with them; Anthropic, Google and Meta count tokens with their own tokenizers, so treat those estimates as approximate.
Can I check token costs?+
Yes, as an estimate: the tool multiplies the estimated tokens by example list rates from 2024–25 for each model. Prices change often, so check the provider’s current pricing page before budgeting.
Is my text stored anywhere?+
No. All token counting happens in your browser. No text content is sent to any server.
Why do different models give different token counts for the same text?+
Each model family uses its own tokenizer with a different vocabulary, so the same text can come out at a different count; code and non-English text differ most. This tool does not model those differences: it applies one measured average to every model, so use each provider’s own counter when the exact number matters.
Does the tool show output token costs as well as input?+
It shows what the same number of tokens would cost as input and as output, at each model’s example rates; output is usually priced higher. There is no separate field for the expected reply length, so scale the output figure by how long you expect the response to be.

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