Prompt Compression Benchmarker: Cut LLM Input Costs by 35–63% With Measurable Quality Tracking

Prompt Compression Benchmarker, a tool built autonomously by NEO.
This tool benchmarks major prompt compression methods against your actual workload to cut input token costs by 35 to 63 percent.

Most LLM costs come from heavy input tokens like long documents, codebases, and conversation histories.

Instead of blindly pruning data, this tool lets you test algorithms like LLMLingua and TF-IDF directly on your own data to find the optimal balance between cost and quality.

⚙️ How It Works
1. Benchmark on your actual data: Bring your own custom JSONL datasets for specific tasks like RAG, summarization, or coding.

2. LLM-judge the top candidates: Standard proxy metrics like F1 and ROUGE are fast but mechanical.
The built-in LLM judge calls a real model to definitively evaluate whether the compressed context still supports the correct answer.

3. Deploy the winner: The tool automatically exports a one-line drop-in Python wrapper so you can deploy the winning setup instantly for your OpenAI or Anthropic clients.

4. Monitor in production: Ensure long-term stability across your AI pipelines after deployment.

💡 The Real ROI for Premium Models Compression is most valuable when applied to premium models.
If you process 3 million input tokens a day on Claude Opus 4.7, optimizing your context can save you approximately $600 a month on your base costs.
It saves money exclusively on input tokens without changing your output tokens at all.

🛠️ Seamless Developer Integration Stop manually calculating your API savings. The benchmarker ships with an MCP server that integrates directly into Claude Code.

You can simply ask your AI coding assistant to estimate your token savings or recommend the best compressor right inside your development environment.

🔗 Resources & Links Check out the repository on GitHub to start benchmarking your pipeline today: https://github.com/dakshjain-1616/Prompt-Compression-Benchmarker

You can also build with NEO in your IDE

VS Code: https://marketplace.visualstudio.com/items?itemName=NeoResearchInc.heyneo

Cursor: https://open-vsx.org/extension/NeoResearchInc/heyneo

You can use use NEO MCP with Claude Code: https://heyneo.com/claude-code

#promptengineering #MachineLearning #AI #CostOptimization #SoftwareEngineering #LLM #python #llms

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