Prompt Length Optimizer

Free Tools  /  AI Productivity

Prompt Length Optimizer

Every unnecessary word in your prompt costs tokens — and at scale, filler phrases quietly inflate your API bill. This free prompt length optimizer scans your text for classic wordy constructions like “due to the fact that” and “it is important to note that,” then rewrites them into tighter equivalents automatically. You get a side-by-side view with before-and-after word counts, estimated tokens saved, and a full list of every replacement made. The meaning stays intact; the bloat doesn’t. Use it to trim prompts before pasting them into ChatGPT, Claude, or any API call.

0 of 3 free uses

Detects wordy phrases like “due to the fact that” and rewrites them automatically. Meaning is preserved — only filler is removed.

Tightened prompt

0
Words before
0
Words after
0
Est. tokens saved
0%
Reduction

      
      

Replacements made

Frequently asked questions

Why shorten prompts?

Three reasons: cost (API providers bill per token, and every filler word adds up over thousands of calls), clarity (shorter prompts keep the model’s attention on your actual instructions), and context limits (trimming leaves more room for the response and for conversation history).

Will shortening hurt quality?

No — this tool only removes filler: wordy phrases, hedges, and redundancies that add zero meaning. Your instructions, examples, and constraints stay exactly as they are. In fact, tighter prompts often get better answers because the key instructions stand out.

What’s the ideal prompt length?

It depends on the task, but most effective prompts land between 50 and 200 words. Simple tasks need less; complex multi-step work needs more. The rule of thumb: include everything the model needs to do the job well, and nothing it doesn’t.

Does this work for any model?

Yes. Every major AI model processes text as tokens, so cutting filler saves tokens and cost on ChatGPT, Claude, Gemini, Grok, DeepSeek, and API calls alike. The rewritten phrasing is plain English that all models understand.

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