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Prompt efficiency

Paste any prompt. ContextCrunch rewrites it to say the same thing in fewer tokens — using Groq Llama 3.3 70B and the math of mutual information.

Live prompt efficiency analyzer & compressor Groq Llama 3.3 70B
0tokens
—entropy
0%redundancy
Compressed prompt — ready to paste

Common token waste patterns

Polite filler
"I was hoping you could please help me understand and explain in detail..."
→ "Explain..." saves ~12 tokens
Redundant context
"As we discussed, as I mentioned, as you know, as I said before..."
→ Remove entirely, saves ~15 tokens
Over-specification
"...that is helpful and informative and useful and relevant and accurate..."
→ Implied, saves ~18 tokens
Restating the obvious
"I am a human user asking you, an AI assistant, to help me with..."
→ The model knows, saves ~20 tokens

Compress your full conversation, not just a single prompt.

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