AI Prompt Compressor: Reduce Tokens & Save on LLM Costs

AI prompt compressor online: compress prompts by removing filler and boilerplate to reduce tokens and cut LLM API costs.

Updated 2026-08-16

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Features

Four-stage rule engine: removes filler words, strips greetings and boilerplate, deletes empty modifiers, and merges duplicate instructions
Real-time token counting with js-tiktoken (cl100k_base) and a Chinese-aware estimator: see savings the moment you type
Three compression strengths: Mild strips polite fillers only, Standard also cuts greetings and modifiers, Aggressive merges duplicate instructions
Removed-fragment list with per-item restore: uncheck any snippet to bring it back into the output, or restore everything in one click
Savings stats at a glance: tokens before/after, saved tokens, savings percentage, character count, and line count
Bilingual rule lists: English and Chinese filler words, greetings, and boilerplate are both fully supported
Honest by design: rules-based compression may drop subtle nuance, so the UI reminds you to review important prompts before use
One-click copy, or save the compressed prompt as a .txt file for later reuse
Drag-and-drop or open .txt/.md prompt files directly into the editor

How to Use

1Paste your prompt into the left panel: compression runs automatically as you type, no button to press.
2Pick a strength from the top bar: start with Standard for everyday prompts, Mild for a light touch, Aggressive for maximum savings.
3Read the stats cards in the right panel: tokens before/after, saved tokens, and savings percentage update in real time.
4Open the Removed fragments list and uncheck any snippet you want to keep: the output rebuilds instantly.
5Click Restore to bring every removed fragment back, or Copy / Save to export the compressed prompt as a .txt file.
6Teaching: compress a long system prompt before deploying it; shorter prompts cut per-request API cost across thousands of calls.
7Teaching: before an API call, run the conversation history through the compressor to fit more context into the model's window.
8Teaching: turn wordy requirement notes into crisp instructions; "Could you please make sure to..." becomes a direct command.
9Tips: avoid Aggressive mode for creative writing or tone-sensitive content; Mild and Standard preserve more of the original voice.
10Always read the compressed result once before using it in production; the honest-note banner reminds you why.
11Try the example buttons below the input (Verbose System Prompt, Customer Support Prompt, Chinese E-commerce Prompt) to see each strength in action.

Frequently Asked Questions

Will compression change the meaning of my prompt?

Rules-based trimming only deletes redundancy (filler words, greetings, empty modifiers, and duplicate instructions); it never rewrites or reorders your text. But this is not semantic compression: an AI-powered compressor can rephrase to save far more tokens, while this tool's savings are transparent (every cut is listed and restorable) and modest. Always review important prompts once before using them.

How many tokens can I save?

Typically 15-40% for most prompts, depending on how padded the original is. Very verbose prompts with lots of polite filler can save more than half; already-concise prompts may save almost nothing. The stats cards show your exact savings in real time.

Does it support Chinese prompts?

Yes. The engine ships separate English and Chinese rule lists for filler words, greetings, and modifiers, so both languages (and mixed prompts) are compressed the same way. Token counting for Chinese uses the same estimate as the site's token calculator: about 1.5 tokens per character plus punctuation.

What is the difference between this tool and ai-answer-compressor?

This tool compresses the prompt you send to the AI: your input side, which determines input-token cost. An answer compressor (ai-answer-compressor) shortens what the AI returns: the output side. Use this tool before calling an API, and an output compressor after.

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 other models with different tokenizers.

What are the risks of Aggressive mode?

Aggressive mode merges near-duplicate instructions and removes more fillers, which can over-delete and shift the tone of your prompt. It is best for long, repetitive system prompts. For normal use, Standard mode offers the best balance of savings and fidelity.

Can it compress code or JSON inside prompts?

Code itself is never touched; the engine only removes surrounding filler and duplicate instruction lines. Avoid Aggressive mode when your prompt contains several similar-looking lines of code, since near-identical instructions could be merged by mistake.

Can I use the compressed prompt as-is?

Yes, the output remains a complete, valid prompt you can paste directly into ChatGPT, Claude, or an API call. The removed-fragments list lets you restore any part you miss, so you are never locked into a bad cut.

Can the ai prompt compressor compress prompts for Claude and other models?

Yes. The rules-based engine is model-agnostic; it removes filler and duplicates regardless of which model you target. The token counter uses cl100k_base (GPT-4/ChatGPT) as a reference, so Claude, Gemini, and others may count a few percent differently, but the savings are real on any tokenizer. After compressing, review the result once and test it with your target model.

Why can't I shrink my prompt enough to fit the context window?

Rules-based compression only removes redundancy (typically 15-40%), so if you need to squeeze a 50,000-token document into an 8,000-token window, this tool won't get you there on its own. For that, split the content into chunks, summarize the middle parts, or use a model with a larger window. Use this tool first to trim the fat, then handle the structural reduction separately.

How much does ai prompt compression save on API costs?

Input tokens are billed per million (roughly $0.15-$5 per 1M depending on the model). If you send 1M prompt tokens per day and compression saves 30%, that's 300K tokens saved daily, about $0.45-$1.50 per day on mid-range pricing, and much more on expensive models. Multiply saved tokens × your price per 1M to get your exact number. Output tokens are unaffected since this tool only touches the input side.

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

The engine flagged that fragment as filler, greeting, modifier, or a near-duplicate of another instruction. Every fragment can be restored with a single click, so you can compare with and without it before finalizing.

Why does my compressed prompt sound unnatural or choppy?

Because rules-based compression deletes words instead of rewriting them. Aggressive mode cuts the most, so sentences can come out clipped or choppy; Mild and Standard keep more of the original phrasing. If a sentence reads badly, restore that fragment from the Removed fragments list or reword it yourself; the trade-off is token savings for naturalness, so tone-sensitive copy is safer with Mild or Standard.

Will token counts differ between AI models?

Yes, slightly: every model family uses its own tokenizer. cl100k_base (GPT-4/ChatGPT) is used as the standard here; Claude, Gemini, and others may differ by a few percent. The compression itself is model-agnostic and saves tokens on any tokenizer.

After compressing, my AI's answers got noticeably worse: what went wrong?

Most likely the wrong strength for that prompt: Aggressive mode merges near-duplicate instructions and strips more tone, which can remove context the model was actually using. Fix it by switching to Mild or Standard, opening the Removed fragments list and restoring anything that looks load-bearing, then re-testing the same prompt with your model. If savings are still low after that, the prompt was already concise; that is the honest ceiling of rules-based compression (typically 15-40%).