Skip to main content
UFOZoo

AI Answer Compressor: Trim Filler Words & Save Tokens

AI answer compressor online: remove filler words and redundant phrases from AI responses to save tokens and fit context limits.

Updated 2026-08-16

Related Tools

AI Output Cleaner: Fix Formatting & Remove AI Tics

AI Prompt Compressor: Reduce Tokens & Save on LLM Costs

AI Content Detector: Check ChatGPT, Gemini & Copilot Text

AI Code Extractor: Pull Code, URLs & Tables from Chat

AI Humanizer: Make AI Text Sound Human & More Natural

AI Image Detector: Spot AI-Generated Photos Online

Features

  • Greeting removal: strips line-start openers like Certainly!, Sure, Here is, and their Chinese equivalents (好的, 当然, 没问题)
  • Closing boilerplate removal: drops Hope this helps, In conclusion, Let me know if you have questions, and Chinese closers like 希望对你有帮助
  • Repeated-conclusion detection: flags an ending paragraph that restates the opening one, using a conservative word-overlap heuristic
  • Filler-word compression in three strengths: Mild trims the lightest padding, Standard cuts very/really/非常/真的, Aggressive strips the rest
  • Hard protection: fenced code blocks, inline code, URLs, numbers and prices, and list structure are never touched
  • Per-fragment restore: every removed snippet is listed with its reason; uncheck any to bring it back, or restore all in one click
  • Real-time token comparison: js-tiktoken (cl100k_base) with a Chinese-aware estimator shows exact savings as you type
  • Bilingual rule lists: English and Chinese greetings, closers, and filler words are both fully supported
  • Honest by design: this is rules-based deletion, not an AI summary; no LLM runs, and the UI reminds you to review important answers
  • Copy the result or save it as a .txt file

How to Use

  1. 1Paste an AI answer (ChatGPT, Claude, Gemini…) into the left panel; trimming runs automatically as you type, no button to press.
  2. 2Pick a strength from the top bar: Standard fits most answers, Mild for a light touch, Aggressive when you want maximum savings.
  3. 3Read the stats cards: tokens before/after, saved tokens, and savings percentage update in real time.
  4. 4Open the Removed fragments list: each snippet shows why it was cut (greeting, closing boilerplate, repeated conclusion, or filler words).
  5. 5Uncheck any fragment to restore it, click Restore all to bring everything back, then Copy or Save the compressed answer as a .txt file.
  6. 6Teaching: compress a long ChatGPT reply before pasting it into a document, and opening niceties and closing pleasantries disappear.
  7. 7Teaching: shorten a Claude answer before forwarding it in an email, so the recipient gets the key points immediately.
  8. 8Teaching: run a wordy answer through Aggressive mode to get a summary-length version, then restore any point that matters.
  9. 9Code blocks, URLs, and numbers stay intact, so the trimmed result is safe to use in technical notes and docs.
  10. 10Always review the output once; rules are not AI understanding, and the honest-note banner is there for a reason.

Frequently Asked Questions

Is this an AI summary?

No. This tool deletes redundant fragments with fixed rules: greetings, closing boilerplate, repeated conclusions, and filler words. No LLM runs and nothing is rewritten, so the output is always a subset of your original answer. This is a transparent trim, not semantic compression or AI summarization: every cut is listed and restorable. For important answers, review the result once before using it.

Will it delete important content?

Only boilerplate is removed: line-start greetings, closing pleasantries, a repeated conclusion, and filler words. Code blocks, inline code, URLs, numbers, prices, and list structure are protected and never touched. And because every deletion is recorded in the Removed fragments list, you can restore anything with one click.

How is this different from prompt-compressor?

prompt-compressor shrinks the prompt you send to the AI, your input side, which drives input-token cost. This tool trims the answer the AI returns, the output side. Use prompt-compressor before calling an API and this tool after, to cut costs on both ends.

Does it work with Chinese answers?

Yes. The engine ships separate English and Chinese rule lists for greetings, closers, and filler words, so Chinese answers (好的/当然, 希望对你有帮助, 非常/很/真的) are trimmed the same way as English ones, including mixed-language answers.

How is repeated-conclusion detection done?

It compares the last paragraph against the first paragraph using word-overlap (Jaccard similarity). Only a very close match (0.6 or higher) is removed, and paragraphs containing numbers, URLs, code, or lists are never candidates. It is a heuristic, so it may occasionally miss a repetition, but it cannot destroy content you cannot restore.

How are tokens counted?

English and Latin text is counted with js-tiktoken using cl100k_base, the same tokenizer family behind GPT-4 and ChatGPT. Chinese text uses the project's standard estimate of ~1.5 tokens per character plus punctuation. Counts are approximations for models with different tokenizers.

Are code blocks safe?

Yes. Everything inside fenced code blocks (``` and ~~~) is skipped entirely: no greeting, filler, or cleanup rule ever runs there, and indentation is preserved. Inline code (`like this`) and URLs are protected too, so technical answers stay valid after trimming.

What about numbers, URLs, and lists?

They are protected. Numbers, percentages, and prices are never removed, URLs (including in filler-like words inside them) are left untouched, and list or table structure is never deleted or reflowed. The only thing that changes is redundant phrasing around them.

Why is one of my sentences shown in the removed list?

The engine flagged that fragment as a greeting, closing boilerplate, repeated conclusion, or filler words. Every fragment carries its reason label, and each one can be restored with a single click so you can compare with and without it before finalizing.

Can I compress a whole document, or only one answer?

Any pasted text works, but the tool is designed for single AI answers: the rules remove greetings, closing boilerplate, repeated conclusions, and filler words, so a long document with none of those patterns will barely shrink. Use Aggressive strength for documents, and keep in mind that protecting numbers, URLs, and code blocks means those stay untouched wherever they appear.

Why does my token count differ from the API's usage report?

English and Latin text is counted with js-tiktoken's cl100k_base (the tokenizer behind GPT-4 and ChatGPT), so counts match OpenAI closely but can differ for Claude, Gemini, or DeepSeek, which use their own tokenizers. Chinese uses the standard estimate of ~1.5 tokens per character plus punctuation, which is an approximation. Treat the number as a good guide, not an exact bill.

What if the rules cut too much?

Switch to a lighter strength (Mild only removes the least risky fillers) or restore fragments individually from the list. The repeated-conclusion heuristic is deliberately conservative, and code, numbers, and URLs are always protected, so over-trimming is limited to phrasing you can bring back.