Test, Analyze & Optimize LLM Token Usage
| Content Type | Avg Tokens | After Reduction | Saved | Efficiency |
|---|---|---|---|---|
| Email → Prompt | 156 | 52 | 104 | 66.7% |
| Marketing Copy | 143 | 58 | 85 | 59.4% |
| Technical Docs | 187 | 78 | 109 | 58.3% |
| Casual Text | 65 | 32 | 33 | 50.8% |
| Code Comments | 98 | 62 | 36 | 36.7% |
📊 Real-time monitoring of all token reductions. Shows the latest activity with prompt preview, timestamp, user, and performance metrics.
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Removes 27 types of filler words like "basically", "actually", "really", "very" and replaces 18 redundant phrases with concise alternatives.
Simplifies sentence structures by removing unnecessary words while preserving meaning. Eliminates "there is/are" constructions and optional words.
Detects and removes duplicate or highly similar content (85%+ similarity). Keeps only unique information and eliminates repetitive sentences.
Applies all three techniques in sequence for maximum reduction. Typically achieves 40-60% token reduction while maintaining core meaning.
Uses hybrid word and character-based estimation: tokens ≈ words × 1.3 or characters ÷ 4. Takes the higher value for accuracy.
Reducing tokens directly reduces API costs. At $0.03/1K tokens (GPT-4), 42.5% reduction saves $12.75 per million tokens monthly.
https://sinsub.online/edtoken
User-friendly interface with visual comparison
POST /edtoken/api.php?action=reduce
JSON input/output for automation
https://sinsub.online/redtoken
Testing and performance monitoring