Completion Token Counter
Estimate the number of tokens an LLM completion or response will generate.
About Completion Token Counter
Completion token estimation helps predict API costs and ensures the model has enough room within the context window for its response. English text averages about 1.33 tokens per word with BPE tokenizers.
Formula
Completion Tokens
tokens = expected_words × tokens_per_word
- tokens_per_word
- Average tokens per word (≈1.33 for English)
Worked example
With the default values below, Completion Token Counter returns Estimated Completion Tokens of 200. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Expected Response Wordsaccepts 1–100,000
- 150
- Tokens per Wordaccepts 0.5–3
- 1.33
Results
- Estimated Completion Tokens
- 200
Step by step
- 1Completion tokens: words × tokens/word= 200
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
Can I control how many tokens the model generates?
Yes — most APIs have a max_tokens parameter that caps completion length. Setting it prevents runaway generation and controls costs.
Are completion tokens more expensive than prompt tokens?
With many providers (OpenAI, Anthropic), completion tokens cost 2-4x more per token than input/prompt tokens.