Compresr vs LLMLingua

Compresr vs LLMLingua: a maintained, query-aware LLMLingua alternative

LLMLingua is excellent research code you run yourself; Compresr is the hosted, query-aware, on-prem-ready version with support and benchmarks: pick LLMLingua for full local control, Compresr to ship without operating the model.

Side-by-side comparison

Both compress prompts and long context. The differences are about operations, query-awareness, accuracy at matched ratios, and how you deploy.

Compresr versus the LLMLingua family across form factor, query-awareness, maintenance, benchmark accuracy, on-prem support, and pricing.
DimensionCompresrLLMLingua / LLMLingua-2
Form factorHosted API + Python/TypeScript SDK + on-prem imageResearch library you host, run, and operate yourself
Query-aware compressionYes, query-specific (latte_v1)Only LongLLMLingua; LLMLingua-2 is query-agnostic
Maintained & tunableCompany-backed, versioned, supportResearch code; effectively unmaintained
QMSum accuracy @ ~2x59.6%LLMLingua-2: 50.7%
FinanceBench accuracy @ ~2x77%LLMLingua-2: 70%
On-prem / in-VPCYes, runs in your VPC, custom volume pricingDIY: self-host the library yourself
Pricing$0.10 / 1M tokens; $10 free credits, no cardFree code + your own GPU/compute and ops time

Figures measured under our harness on single-shot long-document QA (FinanceBench, QMSum), where the full document is compressed before the answer model sees it, not a RAG pipeline. Dated 2026-04. Competitor numbers measured at a matched compression ratio. Single-run accuracy deltas under ~2 points are within noise.

When LLMLingua makes sense

A fair read: the open LLMLingua family is a genuinely good fit in several situations, and we would point you there.

  • You are doing research. You want to read, modify, and cite the actual compression algorithm. LLMLingua and LLMLingua-2 are published, inspectable, and built for exactly that.
  • You need full local control. No external API at all, model weights on your own machines, every line under your governance. Self-hosting an open library is the cleanest way to get there.
  • You have spare compute and no budget. If you already have idle GPUs and the ops capacity to run and maintain the model, free code can be the right trade.

If instead you want query-aware compression that ships today, without standing up and maintaining a model, that is where Compresr fits. Compresr is also complementary to prompt caching and works on context that is unique per request.

Migrating from LLMLingua

Replace your self-hosted compression call with a single Compresr SDK call. Pass your query so the compressor keeps the answer-bearing tokens.

Python: compresr SDK
from compresr import CompressionClient

client = CompressionClient(api_key="...")

# Send the long context plus your query; get back a shorter
# context that keeps the answer-bearing tokens.
result = client.compress(
    text=long_context,
    query="What was Q3 net revenue?",
    model="latte_v1",
    target_compression_ratio=2,  # ~2x, the light-compression sweet spot
)

answer = call_your_llm(prompt=result.compressed_text)

See the quick-start guide for install, auth, and the full response shape.

Frequently asked questions

Is Compresr a drop-in replacement for LLMLingua?
For most use cases, yes. LLMLingua is a research library you self-host and operate; Compresr is a hosted API (with SDKs and an on-prem image) that does query-aware compression. You replace your local LLMLingua call with a single client.compress(text=..., query=..., model="latte_v1") call. The main thing you give up is running the model on your own hardware for free.
How does accuracy compare at the same compression ratio?
At a matched ~2x ratio under our harness (single-shot long-document QA, not RAG, dated 2026-04): on QMSum, Compresr scored 59.6% vs LLMLingua-2 at 50.7%; on FinanceBench, Compresr scored 77% vs LLMLingua-2 at 70%. Light compression (~2x) is where the accuracy story lives.
Does more compression mean the same accuracy?
No, those are two separate claims. High compression ratios (up to ~90% reduction / high Nx) are a cost and latency story. The accuracy win is at light compression (~2x), where cutting noise can match or beat full-context answers. At ~8.9x on FinanceBench accuracy drops to 65%, below the 73% full-context baseline.
Can I run Compresr on-prem like a self-hosted library?
Yes. Compresr offers an on-prem deployment that runs inside your own VPC with custom volume pricing, so you keep data residency and control similar to self-hosting LLMLingua, without maintaining research code.
When should I still use LLMLingua instead?
If you are doing research, need full local control of the model weights and code, want to modify the compression algorithm itself, or have zero budget and spare GPU capacity, the open LLMLingua family is a reasonable choice.