Why compare token counts between text versions?

When optimizing prompts, you want to reduce token count without losing meaning. The token diff shows exactly how many tokens each revision saves or adds, making it easy to measure prompt optimization progress.

Do different models tokenize the same text differently?

Yes. GPT models (BPE tokenization) and Claude models (byte-level BPE) produce different token counts for the same text. This tool lets you select the target model so counts reflect the appropriate tokenization heuristics.

What does a negative token difference mean?

A negative difference means Text B uses fewer tokens than Text A — a good outcome when optimizing prompts. A positive difference means Text B is longer. The color coding (green = fewer, red = more) makes this immediately visible.

AI Token Diff

Compare token counts between two text versions for any AI model.

Model

Text A0 tokens

Text B0 tokens

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guide:token counting guide:prompt engineering basics

FAQ

Why compare token counts between text versions?: When optimizing prompts, you want to reduce token count without losing meaning. The token diff shows exactly how many tokens each revision saves or adds, making it easy to measure prompt optimization progress.
Do different models tokenize the same text differently?: Yes. GPT models (BPE tokenization) and Claude models (byte-level BPE) produce different token counts for the same text. This tool lets you select the target model so counts reflect the appropriate tokenization heuristics.
What does a negative token difference mean?: A negative difference means Text B uses fewer tokens than Text A — a good outcome when optimizing prompts. A positive difference means Text B is longer. The color coding (green = fewer, red = more) makes this immediately visible.

Compare token, word, and character counts between two text blocks. See the token difference with color-coded indicators — red when text B uses more tokens, green when it uses fewer. Useful for optimizing prompts by comparing revisions.