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Token Count Estimate Calculator

Work out token count estimate instantly with clear inputs, formula shown and shareable results.

About Token Count Estimate Calculator

Byte-pair encoders split English into pieces averaging about four characters, equivalently around 1.33 tokens per word. Estimating from both characters and words and averaging the two is more robust than either alone, because character counts overestimate for text full of short words and word counts underestimate for code, URLs and non-English scripts.

Formula

Token heuristics

tokens ~ characters / 4; tokens ~ words / 0.75; blended = average of both estimates

Worked example

With the default values below, Token Count Estimate Calculator returns Estimated tokens of 2,833. Change any input above and every figure here is recalculated in your browser.

Inputs used

Characters of textminimum 1
12,000
Characters per tokenaccepts 1–12
4
Word countminimum 1
2,000

Results

Estimated tokens
2,833
Character-based estimate
3,000
Word-based estimate
2,667
Tokens per word
1.417

Step by step

  1. 1Values used
  2. 2Token heuristics
  3. 3Estimated tokens= 2,833
  4. 4Character-based estimate= 3,000
  5. 5Word-based estimate= 2,667
  6. 6Tokens per word= 1.417

Assumptions and limits

  • The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
  • In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
  • Figures are rounded for display to 3 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

How wrong can the heuristic be?

For English prose it is usually within 10 percent. For code, JSON, or languages like Thai and Japanese it can be off by two to three times, so tokenise exactly when cost matters.

Do different models tokenise differently?

Yes. Vocabulary size and merge rules differ per model family, so the same text can cost noticeably different token counts across providers.

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