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Prompt compression benchmarks

Representative to measure. Targeted to learn. Frozen to prove.

Prompt compression should be evaluated as a savings-quality curve by workload—not as one universal similarity score or a best-case saved-token result.

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✓Net savings after fees and caching
✓Information preservation
✓Grounding and task performance
✓Latency and fallback rate

Measure

Use a representative audit sample.

Estimate production quality and savings from a stratified sample that reflects real prompt lengths, tasks, tenants, and risk.

  • Freeze the measurement window before tuning
  • Report distribution, not only averages
  • Keep quality dimensions separate

Learn

Use sentinels to find failures quickly.

Risk-triggered examples are useful for discovering weaknesses but should not be reported as unbiased production quality.

  • Oversample numbers, negations, schemas, and identifiers
  • Classify risky removed spans
  • Turn repeated failures into explicit protections

Prove

Retest on a frozen holdout.

After protection or profile changes, prove improvement on prompts that were not used to choose those changes.

  • Run the same downstream model configuration
  • Compare original and compressed outputs
  • Publish fallback, latency, and critical-failure rates

Publication status

Methodology first; workload results follow evidence.

This page documents the benchmark contract UsageTap will use for public results. It intentionally does not invent universal savings or quality claims before frozen, workload-specific evidence exists.

Benchmark the decision your production team actually faces.

Measure whether compression creates worthwhile net savings at an acceptable quality, grounding, latency, and fallback threshold.

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