Token savings leaderboard
Not all prompt compression is equal. Stripping whitespace might save two percent while removing a redundant few-shot block saves forty. This tool applies each common technique to your actual prompt, measures the tokens it saves, and ranks them — so you spend your effort on the change that moves the bill, not the one that feels tidy.
How it works
The tool tokenizes your original prompt, then applies each technique independently: trimming few-shot examples, shortening verbose instructions to concise equivalents, collapsing whitespace and formatting, minifying embedded structured data, and removing filler phrases. It measures the token reduction from each, ranks them highest-first, and shows the combined result when you stack the ones you enable. Everything runs locally.
What each technique targets
Few-shot example trimming removes or shortens the input-output examples you include to guide the model. Few-shot examples are among the largest single items in a prompt — three detailed examples might be 400 tokens of your total. The leaderboard will usually rank this near the top if your prompt contains examples.
Instruction shortening rewrites verbose instruction phrases into compact equivalents. “Please make sure that you always respond in a clear and professional manner” → “respond clearly and professionally”. Both convey the same constraint; the second uses far fewer tokens.
Whitespace compression collapses multiple blank lines, trailing spaces, and redundant indentation. This rarely saves more than 5–8% but costs nothing in quality and is always safe to apply.
Structured data compaction minifies embedded JSON, YAML, or similar payloads. A pretty-printed JSON block with four spaces of indentation uses significantly more tokens than compact JSON. If you are embedding schema definitions or data samples in your prompt, this is often a hidden win.
Filler removal strips hedging language — “perhaps”, “I would like you to”, “as mentioned above” — that adds tokens without changing what the model does.
The quality trade-off
The leaderboard is deliberately separate from a decision about whether to apply each technique. Few-shot removal may save the most tokens but also has the highest risk of degrading output quality on nuanced tasks. Whitespace compression is the opposite: nearly zero risk. Apply changes from the top of the leaderboard selectively, re-test against a representative set of inputs, then decide which savings are worth keeping.
Tips and notes
Start at the top of the leaderboard — the biggest single saving is usually few-shot reduction or cutting a redundant instruction block, not micro-edits. But the highest-saving techniques are also the riskiest for quality, so apply them and re-run your evaluation set before shipping. Whitespace and structured-data compaction are nearly always safe and free wins. If your prompt is mostly a fixed system message, also check whether prompt caching makes the whole question moot for the repeated portion. Treat the savings as close estimates and verify behavior, not just token count.