Prompt Token Counter
Count the exact number of tokens used by a prompt before sending it to an LLM.
About Prompt Token Counter
Token counting estimates how many tokens a prompt will consume. Most modern LLMs use BPE tokenization where ~4 characters equals one token for English text. Knowing exact token usage helps stay within context limits and predict costs.
Formula
Token Estimate
tokens ≈ character_count / chars_per_token
- chars_per_token
- Average characters per token (≈4 for English BPE)
Worked example
With the default values below, Prompt Token Counter returns Estimated Tokens of 21. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Prompt Text
- Explain the theory of relativity in simple terms suitable for a high school student.
- Avg Characters per Tokenaccepts 1–10
- 4
Results
- Estimated Tokens
- 21
- Characters
- 84
- Words
- 14
Step by step
- 1Character count= 84
- 2Token estimate: chars ÷ chars/token= 21
Assumptions and limits
- The result is derived only from the 2 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 whole numbers, 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
Why is this an estimate?
Exact token counts depend on the specific tokenizer (GPT uses tiktoken, Claude uses its own). The 4-char rule is a reliable heuristic for English but varies by language.
How do special characters affect token count?
Special characters, code, and non-English text often tokenize less efficiently (more tokens per character), so the actual count may be higher than the estimate.