Token Optimizer
Reduce token usage and API costs by optimizing your prompts without losing meaning.
Original Text
~67 tokensOptimized Text
💡 Token Optimization Best Practices
Remove Politeness in System Prompts
Models don't need 'please' or 'thank you'. Be direct and save tokens.
Instead of "Please analyze this text", use "Analyze this text"
Use Abbreviations
Use common abbreviations where context is clear.
Use "info" instead of "information", "docs" instead of "documents"
Eliminate Redundancy
Don't repeat the same instruction in different ways.
Instead of "Please help me understand and explain", use "Explain"
Use Bullet Points
Lists use fewer tokens than prose for multiple items.
Use '- Item 1\n- Item 2' instead of 'Item 1 and Item 2'
Direct Commands Over Questions
Use imperative mood instead of questions in prompts.
Use "List the benefits" instead of "Can you list the benefits?"
Remove Hedging Language
Avoid qualifiers that don't add value.
Use "This is important" instead of "This might be somewhat important"
Real Cost Impact
Reducing tokens to cut cost
Because you pay per token, trimming a prompt directly trims your bill — and at scale the savings compound fast. Most prompts carry avoidable weight: filler phrases, redundant instructions, verbose examples, and formatting that adds tokens without adding meaning. Optimizing them is one of the highest-leverage, lowest-risk cost reductions available, because it changes nothing about which model you use.
This tool helps you tighten text while keeping its intent, then shows the tokens saved per request and what that adds up to across a realistic request volume. The biggest wins usually come from shortening system prompts (which are sent on every single request), removing unnecessary few-shot examples, and asking the model for concise output.
How to use this tool
- Paste a prompt or system message you send frequently.
- Apply the suggested reductions and review the before/after token counts.
- Multiply the per-request savings by your monthly volume to see the real impact.
Frequently asked questions
Where do token savings matter most?+
In anything sent on every request — especially the system prompt — and in high-volume endpoints. Shaving 100 tokens off a prompt that runs a million times a month is a meaningful, recurring saving.
Will shortening prompts hurt quality?+
Not if you remove redundancy rather than substance. Modern models follow concise instructions well; the goal is to cut filler and repetition, not the actual task description or essential constraints.