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  2. Token Counter

Token Counter

Estimate token counts for LLMs (GPT, Claude, Llama, Gemini)

~0
Tokens
0
Words
0
Characters
1
Lines
Context Window Usage0 / 128,000 tokens
0.00% used128,000 tokens remaining

Estimated API Cost

As Input
$0.000000
$10/1M tokens
As Output
$0.000000
$30/1M tokens

Load Sample Text

Note: Token counts are estimates based on character and word heuristics. Actual token counts may vary by 5-15% depending on the specific tokenizer used by each model. For precise counts, use the official tokenizer libraries (tiktoken for OpenAI, etc.).

Related Tools

  • Word Counter - compare word vs token counts
  • AI Text Cleaner - reduce token usage
  • JSONL Formatter - prepare fine-tuning data
  • Prompt Diff - compare prompt efficiency

How to Use Token Counter

  1. Enter your text

    Paste the text you want to count tokens for.

  2. Select the model

    Choose GPT-4, Claude, Llama, or other models for accurate token counts.

  3. View token statistics

    See token count, character count, and estimated API costs.

Frequently Asked Questions

Is this Token Counter free to use?
Yes, the Token Counter is completely free with no limitations. Count tokens for any text and any model without registration or usage restrictions.
Is my text data private?
Yes, token counting happens entirely in your browser using JavaScript tokenizer implementations. Your text is never sent to any server, which is crucial when working with proprietary content or sensitive information before sending it to AI APIs.
What are tokens and why do they matter?
Tokens are the basic units that AI language models use to process text. They are not exactly words - a token might be a word, part of a word, or punctuation. Understanding token counts helps you stay within model context limits, estimate API costs, and optimize your prompts for efficiency.
Why do different models count tokens differently?
Each AI model family uses its own tokenizer with different vocabularies. GPT models use tiktoken, Claude uses its own tokenizer, and other models have their own implementations. The same text can have different token counts across models, which affects pricing and context window usage.
How do I reduce my token count?
To reduce tokens: remove unnecessary whitespace and formatting, use shorter synonyms, be concise in instructions, avoid repetition, and remove boilerplate text. The tool shows real-time counts so you can see the impact of your changes immediately.