---
title: Compresr vs. LLMLingua
url: https://compresr.ai/compare/llmlingua
description: Compresr is a hosted, query-aware, on-prem-ready LLMLingua alternative; LLMLingua is research code you self-run.
---

# Compresr is a hosted, query-aware, on-prem-ready LLMLingua alternative; LLMLingua is research code you run yourself.

> Compresr is a managed, query-aware context-compression API with on-prem/VPC deployment; LLMLingua is open research code you host and maintain yourself.

> Human-readable page: https://compresr.ai/compare/llmlingua

## At matched ~2x ratio (QMSum, FinanceBench)
- QMSum: Compresr 59.6% vs LLMLingua-2 50.7%.
- FinanceBench: Compresr 77% vs ~70% at comparable settings.
- Form factor: Compresr is a hosted API + on-prem/VPC; LLMLingua is self-run research code.

## When LLMLingua makes sense
- You want fully local, open research code and will own hosting, tuning, and maintenance.

## Accuracy vs. ratio: two separate claims
- The accuracy win lives at LIGHT (~2x) compression: FinanceBench 73%→77%, QMSum 55.9%→59.6% (single-shot long-document QA, not RAG, 2026-04).
- High ratios (~10x / ~90% reduction) are a COST + LATENCY claim only. Past ~2x, accuracy falls ~2pp per doubling and can drop below the full-context baseline (e.g. FinanceBench ~8.9x = 65%).
- These are never combined into a single "90% reduction at full accuracy" claim.

## Related machine surfaces
- [/compare/prompt-compression-tools.md: full roundup](/compare/prompt-compression-tools.md)
- [/benchmarks.md: results](/benchmarks.md)

## Provenance
Compresr Inc. is a Y Combinator W26 company built by four EPFL-trained founders in San Francisco, California and Europe (Switzerland).
Contact: [compresr.ai/contact](https://compresr.ai/contact).