Prompt compression ratio calculator
Long prompts cost more, run slower, and often bury the instructions that actually matter under filler. But cutting blindly can remove the one sentence the model relied on. This tool measures the information density of your prompt and ranks each sentence by density, so you can see exactly where the slack is before you start trimming.
How it works
The calculator counts words and characters, estimates tokens using the standard four-characters-per-token heuristic, and computes the share of low-information filler words and hedges. It then splits the prompt into sentences and scores each one by density — roughly, the proportion of content-bearing words it contains — and surfaces the lowest-density sentences as prime trimming candidates.
A high filler share or several low-density sentences signals a prompt that can likely be shortened with little or no loss. Everything is computed locally in your browser; nothing is sent anywhere, and the analysis updates as you edit.
What qualifies as filler
The tool flags common low-information patterns that prompt writers accumulate over time without noticing:
- Politeness padding: phrases like “please,” “I would appreciate,” and “thank you in advance” add tokens with no effect on model behavior.
- Meta-commentary: “I want you to understand that…” and “It is important that you…” can almost always be cut, leaving only the instruction itself.
- Hedging verbs: “try to,” “attempt to,” and “feel free to” introduce optional tone where a direct imperative is cleaner and more reliable.
- Redundant restatement: saying the same constraint twice (“always be concise — keep responses short”) consumes extra tokens for no extra reliability.
- Vague qualifiers: “somewhat,” “fairly,” “a bit,” “very” — in isolation these rarely change output and clutter the instruction set.
A worked compression example
Consider this bloated opening sentence:
“I would really appreciate it if you could please try to summarize the following article in a concise and brief manner, keeping things short.”
After removing filler and redundancy:
“Summarize the following article in two sentences.”
The compressed version is far shorter, entirely unambiguous, and — because it specifies “two sentences” — gives the model a more actionable constraint than the vague “concise and brief” pair did.
Tips and notes
Treat the flagged sentences as questions, not commands: “does the model need this to behave correctly?” Often the answer is no — pleasantries, repeated instructions, and meta-commentary like “I want you to” add tokens without changing output. Keep concrete constraints, examples, and format instructions; those usually earn their tokens. After trimming, re-run the prompt on a couple of real inputs to confirm quality held. A good target is to remove filler until the density metrics stop improving easily — past that point you risk cutting muscle instead of fat.