Roundup, 2026

Best prompt compression tools, compared honestly

If you want hosted, query-aware compression with on-prem support and published benchmarks, Compresr is a strong default, but the open LLMLingua family is the right call when you need full local control.

The tools, side by side

Five options worth knowing in 2026. Accuracy is QMSum at a matched ~2x ratio under our harness, except where a tool's published figure sits at a different ratio (noted inline).

Prompt compression tools compared across approach, query-awareness, hosting, on-prem support, maintenance, and QMSum accuracy at a matched ratio.
Dimension	Approach	Query-aware	Hosted	On-prem	Maintained	QMSum @ ~2x
Compresr (latte_v1)	Learned, query-specific compression	Yes	Yes	Yes (in-VPC)	Yes, company-backed	59.6%
LLMLingua-2	Token classification (query-agnostic)	No	No (self-host)	DIY	Research code	50.7%
LongLLMLingua	Perplexity-based, query-aware	Yes	No (self-host)	DIY	Research code	53.7% (@ ~3x)
Selective Context / semantic chunking	Drop low-information spans; chunk + filter	Partial (chunk-level)	No (library)	DIY	Varies / community	Not in our matched run
The Token Company (ttc)	Hosted compression service	Service-dependent	Yes	Vendor-dependent	Commercial	48.2%

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.

How to choose

There is no single best tool, just the best fit for your constraints. Start from what you actually need.

You want to ship, not operate a model

Pick a hosted, maintained service. Compresr and The Token Company are both hosted; Compresr adds query-specific compression, an on-prem image, and public FinanceBench / QMSum numbers.

You need full local control

Self-host an open library. LLMLingua-2 and LongLLMLingua are published and inspectable, ideal for research and bespoke modifications, at the cost of running and maintaining them.

Your answer depends on the query

Choose a query-aware tool (Compresr or LongLLMLingua) so the compressor keeps the tokens that matter for the specific question rather than a generic summary.

You want the most cost & latency cut

Push compression harder for cost and latency, separately from accuracy. And pair compression with prompt caching for repeated prefixes.

Frequently asked questions

What is the best prompt compression tool in 2026?

It depends on whether you want a hosted, maintained service or research code you run yourself. For a query-aware, hosted, on-prem-capable option with published benchmarks, Compresr is a strong default. For full local control of an open algorithm, the LLMLingua family is the reference. The Token Company is another hosted option.

Which compression tools are query-aware?

Compresr (latte_v1) and LongLLMLingua are query-aware: they keep the tokens that matter for your specific question. LLMLingua-2 is query-agnostic. Selective Context and semantic chunking are query-aware only at the chunk-selection level, not token level.

How does Compresr score against other tools?

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 50.7%, LongLLMLingua 53.7% (at ~3x), and The Token Company 48.2%. On FinanceBench at ~2x, Compresr scored 77% vs LLMLingua-2 70%. Single-run deltas under ~2 points are within noise.

Are these RAG comparisons?

No. These benchmarks are single-shot long-document QA: the whole filing or transcript is compressed first, then sent to the answer model, versus sending it in full. They are not retrieval (RAG) comparisons. Compression composes with RAG rather than replacing it.

Does more compression give the same accuracy?

No, keep the claims separate. High compression ratios (up to ~90% reduction) are a cost and latency story. The accuracy benefit shows up at light compression (~2x). Pushed hard (e.g. ~8.9x on FinanceBench, ~65%) accuracy can fall below the full-context baseline.