How is the compression ratio calculated?

The tool measures three sources of waste — duplicate or near-duplicate sentences, high-frequency repeated phrases, and common filler words — then sums the tokens they consume. The ratio is the estimated compressible tokens divided by the total, giving a realistic upper bound on savings from trimming alone.

Is this the same as a prompt compression model like LLMLingua?

No. Dedicated compression models can achieve far higher ratios by dropping low-perplexity tokens with a learned model. This tool gives a fast, transparent heuristic estimate of obvious redundancy, which is a good first pass before reaching for a heavier approach.

Will trimming always preserve meaning?

Removing exact duplicate sentences and filler words is usually safe, but aggressive trimming of repeated phrases can lose nuance. Treat the estimate as a budget and verify that the model still answers correctly after compression, especially for instruction-heavy prompts.

Why compress prompts at all?

Tokens cost money and fill the context window. If your system prompt or retrieved context is sent on every request, even a 20 percent reduction compounds across thousands of calls into real savings and leaves more room for the actual answer.

Prompt Compression Estimator

Find the wasted tokens hiding in your context

If you send the same system prompt or retrieved context on every API call, every redundant word costs money and consumes context-window space at scale. This tool scans your text for the three most common sources of bloat — duplicate sentences, repeated phrases, and filler words — and estimates how many tokens you could cut without rewriting the substance.

How it works

Paste your context and the tool runs three passes. First it splits the text into sentences and flags exact or near-exact duplicates. Second it counts repeated multi-word phrases that appear more often than expected. Third it counts common filler words (“very”, “really”, “in order to”, “it is important to note that”, and similar) that rarely add information for a model. It estimates the tokens each category wastes (roughly four characters per token) and reports a compression ratio — the share of tokens you could plausibly remove by trimming alone.

Tips and example

Treat the ratio as a budget, not a guarantee. Removing exact duplicates and filler is almost always safe; trimming repeated phrases can lose nuance, so verify the model still answers correctly afterward. A context with a 25% estimated compressible share, sent on 10,000 calls a day, is a meaningful cost line — that is exactly where a few minutes of editing pays off. For deeper compression, this heuristic is a good first pass before reaching for a learned compressor like LLMLingua.