Prompt Token Optimizer

About This Tool

The Prompt Token Optimizer scans your prompt for common filler words and verbose phrases that inflate token count without improving model output. It suggests targeted removals — “please,” “kindly,” “in order to” → “to,” “due to the fact that” → “because” — and shows the before/after token count and percentage saved.

Why prompt size matters

Every input token is billed. A 5K-token system prompt sent on every API call to Claude Opus 4.7 costs $0.075 per call. Across 100K monthly calls, that's $7,500/month for the system prompt alone. Trimming 30% of fluff drops it to $5,250 — a $27,000 annual saving from one editing pass.

What to remove

Politeness markers add no instruction signal: “please,” “kindly,” “if you would.” Hedge words dilute commands: “maybe,” “possibly,” “you might want to.” Verbose phrasings can collapse: “in order to” → “to,” “due to the fact that” → “because,” “at this point in time” → “now.” Empty intensifiers add noise: “really,” “very,” “just,” “actually.”

What to keep

Structure tokens earn their cost: XML tags help models locate information, markdown headers organize sections, code fences signal special handling. Few-shot examples are usually worth the bloat. Explicit constraints (“respond in JSON,” “maximum 200 words”) prevent expensive output overruns. Don't cut these.

The compression trade-off

Aggressive trimming can hurt quality. Models trained on natural language sometimes need a bit of context they've seen in training. A prompt that's too terse can produce stilted, off-format output. Trim, test, measure quality on real evals, repeat. Don't deploy untested compressions to production.

Pair with caching for compounding savings

After optimization, cache stable prefixes. Anthropic's prompt cache cuts cached input cost by 90%. A 3.5K-token optimized system prompt cached 100x costs roughly the same as a single uncached call. The math compounds — see prompt caching savings calculator.

Pair with the token counter, character counter, word counter, and LLM cost comparison. For full agent budgeting, see function calling cost calculator.