Enter your text into the calculator to convert it into individual tokens.

Words To Tokens Calculator

Estimate how many tokens your text uses and the API cost.

By word count
Paste text
words

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Words To Tokens Formula

The following formula is used to tokenize the input text.

T = tokenize(W)
  • Where T represents the tokens extracted from the text
  • W is the input text
  • tokenize() is the function that splits the text into words and punctuation

To obtain the tokens, pass your input text through the tokenize function.

Words to Tokens Conversion Table (Approximate, English prose)
Words Tokens (approx.)
57
1013
1520
2027
2533
3040
4053
5067
6080
75100
100133
150200
200266
250333
300399
400532
500665
750998
10001330
20002660
*Approximation uses Tokens ≈ 1.33 × Words (rounded to nearest token). Actual counts vary by tokenizer, punctuation, numbers, and language.

What is a Words To Tokens Calculator?

Definition:

The Words To Tokens Calculator is a tool that breaks down a given input text into individual tokens such as words, punctuation marks, and contractions for easy analysis.

How to Use the Words To Tokens Calculator?

Example Problem:

The following example demonstrates the steps to tokenize a sample sentence.

First, enter your text into the Input Text area. For example, type "Hello, world!" in the text box.

Next, click the "Calculate" button to process the input text.

The calculator will display each token on a new line in the Tokens area.

FAQ

How does the tokenization process work?

The calculator uses a regular expression to split the input text into words, punctuation, and contractions, ensuring each token is captured individually.

Can the calculator handle special characters and multiple languages?

While the calculator is designed to tokenize typical English text accurately, some special characters or non-standard symbols may be treated differently based on the regex pattern.

What happens if no text is entered?

If the input text is empty, the calculator will prompt you to enter some text before attempting to tokenize.