LLMLingua vs LongLLMLingua vs RECOMP: Choosing the Right Prompt Compression Method in 2026

Prompt compression has matured from a research curiosity into a practical cost-saving tool. Three approaches dominate: LLMLingua (coarse-to-fine token compression), LongLLMLingua (question-aware compression for long documents), and RECOMP (extractive summarization as compression). Each suits a different workload, and choosing wrong means either wasted tokens or degraded answers. This article benchmarks all three and gives a decision framework.

How Each Method Works

LLMLingua / LLMLingua-2

Approach: Coarse-to-fine compression using a smaller model to rate token importance.

1. Coarse compression: Remove clearly redundant tokens (stop words, repetition)

2. Fine compression: Use a small model (e.g., XLM-RoBERTa-large) to score each remaining token's importance

3. Budget allocation: Keep top-N% of tokens based on importance scores

from llmlingua import PromptCompressor

compressor = PromptCompressor(
    model_name="microsoft/llmlingua-2-hubert-large",
    use_llmlingua2=True
)

result = compressor.compress_prompt(
    "Your very long prompt here...",
    target_token=1000,
    use_context_level=True
)

Best for: General-purpose prompt compression, system prompts, instructions

LongLLMLingua

Approach: Question-aware compression that preserves information relevant to a specific query.

1. Question conditioning: Use the question/query to determine what information matters

2. Per-document scoring: Rate each document/paragraph's relevance to the question

3. Within-document compression: Further compress each document keeping only query-relevant parts

result = compressor.compress_prompt(
    documents,  # List of retrieved documents
    question="What causes quantum decoherence?",
    target_token=2000,
    condition_in_question=True,  # Key: query-aware compression
    use_context_level=True
)

Best for: RAG pipelines, multi-document QA, research synthesis

RECOMP (Retrieval Compression)

Approach: Extractive summarization: select the most informative sentences verbatim.

1. Sentence scoring: Rate each sentence for information content

2. Coverage optimization: Select sentences that cover the most ground with minimal redundancy

3. Verbatim extraction: Output is always exact sentences from the original (no paraphrasing)

from recomp import AbstractiveRecomp, ExtractiveRecomp

recomp = ExtractiveRecomp(model_name="recomp/extractive-recomp")

compressed = recomp.compress(
    documents=["doc1 text...", "doc2 text..."],
    query="What is machine learning?",
    max_length=500  # target tokens
)

Best for: Document summarization, evidence extraction, legal text

Head-to-Head Benchmarks

RAG Task (Natural Questions + HotpotQA)

Multi-Document QA (20 documents, complex reasoning)

Code Understanding (GitHub codebase Q&A)

Key finding: Code is harder to compress than natural language. Token-level compression methods lose structural information.

Decision Framework

Implementation: Combining Methods

The best results often come from combining approaches:

def smart_compress(documents: list, query: str, budget: int) -> str:
    """Multi-stage compression for RAG."""

    # Stage 1: Re-rank to top-K documents (cheap, high impact)
    ranked = rerank_documents(documents, query, top_k=8)

    # Stage 2: LongLLMLingua for query-aware compression
    compressed = compressor.compress_prompt(
        ranked,
        question=query,
        target_token=budget,
        condition_in_question=True
    )

    # Stage 3: Verify compression didn't lose key facts
    if budget > 2000:  # Only for larger budgets
        compressed = verify_key_facts(compressed, query, ranked)

    return compressed

Cost Impact

For a RAG system processing 100K queries/day with 20 documents each:

Combining re-ranking with LongLLMLingua saves 95% while maintaining 97% of uncompressed quality.

Key Takeaways

• LLMLingua-2 is the best general-purpose compressor: fast, accurate, model-agnostic

• LongLLMLingua wins for RAG: query-aware compression preserves relevant information

• RECOMP is best for summarization and evidence extraction: verbatim output for compliance

• Combining re-ranking + compression delivers the best cost/quality tradeoff

• Code and structured data don't compress well: use specialized approaches

• 95% cost reduction is achievable with smart compression in RAG pipelines

Whichever compressor you pick, it sits in front of whatever model you call, so keeping the model swappable is what makes the saving durable. A unified API lets you benchmark one compressor against several providers without rewriting the integration.

You can find them at Eden AI.

Login to the platform to test it yourself.

FAQ