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

Prompt compression can substantially reduce LLM costs by shrinking inputs while attempting to preserve answer quality, with LLMLingua-2, LongLLMLingua, and RECOMP serving distinct use cases. LLMLingua-2 uses token-importance scoring for fast, general-purpose compression, LongLLMLingua conditions compression on a specific question and performs best in retrieval-augmented generation and multi-document QA, while RECOMP selects informative sentences or produces summaries, making its extractive mode particularly suitable when source-faithful evidence is required. Benchmarks indicate that query-aware LongLLMLingua retains accuracy better than uniform compression for complex document collections, and pairing document re-ranking with it can reduce RAG token costs by about 95% while retaining roughly 97% of baseline quality. RECOMP offers low-latency extractive compression and supports legal, medical, and compliance applications, whereas code and other structured data remain difficult to compress because token-level approaches can remove essential structural information. The recommended approach is to match the compression method to the workload, benchmark it on task-specific data, and combine re-ranking with query-aware compression where appropriate.

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