Prompt Engineering Tokens Cost

Token Counter for AI Prompts

Paste a prompt, get an honest token estimate — a range, not a fake-precise number — plus the cost across GPT, Claude, and Gemini.

Overview

A token is the unit models actually read and bill, and it is neither a character nor a word. This loads a typical assistant prompt and counts it: an estimated token range (because tokenizers genuinely disagree on the same text), the same text's count across four models, and what one call costs versus a thousand. The number is honest about its own uncertainty — that is the point of an estimator that refuses to lie with false precision.

Workflow

  1. Paste the prompt

    Any text — the report shows counts only, never echoes your content.

  2. Read the range

    Tokenizers disagree, so the estimate is a range with the model comparison beside it.

  3. Check the cost

    Per call looks trivial; the per-1,000-calls line is the real budget.

Why This Works

  • A range is honest where a single number would be a lie — tokenizers really do differ
  • Content-type detection adjusts the estimate for prose, code, and CJK
  • The cost line turns an abstract count into a number you can budget against

Best for

  • Getting a quick, honest token count
  • Sanity-checking a prompt before an API call
  • Comparing the same text across models

Not for

  • Deciding whether content fits a context window — that's the Context Window Estimator
  • Counting characters or words for a text limit — that's the Character Counter

Use cases

  • Getting a quick, honest token count
  • Sanity-checking a prompt before an API call
  • Comparing the same text across models

Tip: Save time by exploring related resources and tools that integrate with this workflow.

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