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AI & AutomationGenerated accuracy model review: 2026-05-06

Prompt Token Estimator accuracy and testing

This page documents how Calwarden builds trust for this calculator: source notes, regression-style default tests, limitations, assumptions, unit handling, rounding, and visible change history.

Formula source

  • Text tokens = character count / characters per token. Estimated prompt tokens = text tokens + message overhead + extra context tokens. Monthly prompt tokens = estimated prompt tokens * monthly requests.

Test cases

Default input regression check

Generated from the checked-in default inputs and calculate function.

Inputs

  • Prompt Text: Summarize the following customer conversation and extract action items for the support team.
  • Characters per Token: 4
  • Message Overhead: 50 tokens
  • Extra Context: 1,000 tokens
  • Monthly Requests: 50,000

Expected outputs

  • Estimated Prompt Tokens: 1,073
  • Character Count: 92
  • Monthly Prompt Tokens: 53,650,000
  • Text Tokens: 23

Known limitations

  • This page documents the checked-in calculator logic and does not certify results for every real-world edge case.
  • Use the result as a planning estimate unless the calculator page states a stricter purpose.

Assumptions

  • This is an approximation, not exact tokenization.
  • Different models and languages tokenize differently.
  • Prompt text is not uploaded.

Unit handling

  • Prompt Text accepts text values.
  • Characters per Token accepts numeric values.
  • Message Overhead accepts values in tokens.
  • Extra Context accepts values in tokens.
  • Monthly Requests accepts numeric values.

Rounding rules

  • Number displays use up to 2 decimal places in the calculator UI.
  • Input precision follows configured step values where provided.

Version history

1.0.0 - 2026-05-06

Generated accuracy and testing page from the current prompt-token-estimator calculator definition.

Change log

2026-05-06

Published default regression test documentation and metadata fallbacks for Prompt Token Estimator.

Calculator transparency

Accuracy pages are generated from checked-in calculator definitions and can be upgraded with curated review metadata over time.

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