SuperCompress vs Alternatives

Q: Which method has the lowest latency?

Sliding window has zero latency. SuperCompress adds ~15ms. Top-K and MMR add ~20-50ms. Headroom adds ~100ms plus cold-start warmup. HyDE and Self-RAG add 500ms+ due to additional LLM calls.

A side-by-side comparison of every major prompt compression method — from lightweight scoring to full-model approaches — with benchmarks, trade-offs, and deployment guidance.

At a glance

Method	Approach	Query-Aware	Model Size	Latency	Savings
SuperCompress	Learned policy (5K params)	Yes	~200KB	~15ms	~65%
Headroom	ModernBERT (165M+ params)	No (type-aware only)	~500MB	~100ms + warmup	~50%
Sliding Window	Keep last N tokens	No	0 (rule-based)	~0ms	Fixed 50%
Top-K Retrieval	Embedding similarity	Partial	Embedding model	~50ms	Variable
HyDE	Hypothetical doc + retrieval	Yes	LLM + embedding	~500ms+	Variable
MMR	Diversity sampling	No	0 (algorithmic)	~20ms	Variable
Self-RAG	On-demand LLM retrieval decisions	Yes	LLM	~1s+	Variable

Detailed comparisons

SuperCompress vs Headroom

Headroom uses a 165M+ parameter ModernBERT model with ONNX Runtime. It classifies prompt content by type (JSON, code, text) but does not score against the user's query. SuperCompress is 25,000x smaller and query-aware.

5K params, query-aware 165M+ params, type-only

SuperCompress vs Sliding Window

Sliding window keeps the last N tokens. It's fast and simple but drops older context that may be critical. SuperCompress keeps relevant context regardless of position.

Position-independent Always drops tail

SuperCompress vs Top-K Retrieval

Top-K uses embedding similarity to find relevant passages. It works well for RAG but misses non-vector context like instructions and conversation history.

Full prompt analysis Embedding-dependent

SuperCompress vs HyDE

HyDE generates a hypothetical answer before retrieving. It adds an entire LLM call to the critical path, making it expensive for high-throughput systems.

~15ms, no extra LLM call 2x+ LLM cost

SuperCompress vs MMR

MMR trades relevance for diversity. It prevents redundancy but may drop the single most relevant passage. SuperCompress optimizes for the user's specific question.

Query-optimized Diversity-optimized

SuperCompress vs Self-RAG

Self-RAG lets the LLM decide when to retrieve. It's the most flexible but most expensive approach, requiring the LLM to participate in retrieval decisions.

Deterministic, fast LLM-participating, slow

Why query-awareness wins

SuperCompress's advantages

~5K parameters — 25,000x smaller than the next closest ML-based approach. Installs in milliseconds, not minutes.
True query-awareness — Scores every line against the user's actual question, not just content type. A line about account settings is kept when the user asks about billing, dropped when they ask about features.
No model download — pip install supercompress is fully self-contained. No downloading ModernBERT weights, ONNX Runtime, or ~500MB of dependencies.
Serverless-ready — ~60ms cold start, ~200KB install size. Fits comfortably in AWS Lambda, Cloudflare Workers, and Vercel Edge Functions.
Hosted API + local library — Use the hosted API (free tier available) for zero-infrastructure setup, or the local library for latency-sensitive workloads.
97.5% oracle recall — The compressed prompt produces answers virtually identical to the full-prompt response, even when evaluated against the "oracle" test of whether any dropped content was actually needed.

Which method should you use?

Choose SuperCompress when:

You want the best compression-to-quality ratio (max savings, minimal quality loss)
You need serverless or edge deployment (Lambda, Cloudflare Workers, Vercel Edge)
You don't want to manage a model download or ~500MB of dependencies
You have diverse prompts where relevance varies by user question
You want a simple API: from supercompress import compress

Choose an alternative when:

Headroom: You want a proxy-based solution that wraps your existing LLM client, or prefer MCP integration over a library import
Sliding window: You only care about the most recent tokens and want zero additional latency (e.g., streaming chat)
Top-K / MMR: You already have a RAG pipeline with embeddings and want to optimize retrieval diversity
HyDE / Self-RAG: You need the absolute maximum theoretical recall and have budget for additional LLM calls

Is SuperCompress always better than Headroom?

Not always. Headroom's proxy mode and MCP integration make it easier to add to existing applications without code changes. SuperCompress requires adding a library import or API call. However, in terms of compression quality, deployment footprint, and latency at scale, SuperCompress has clear architectural advantages — see the full comparison for details.

Can I use SuperCompress with other compression methods?

Yes. SuperCompress's query-aware scoring complements other methods. For example, you can apply SuperCompress before a sliding window as a safety net, or use MMR for candidate selection and SuperCompress for final compression. See the guide for advanced patterns.

Which method has the lowest latency?

Sliding window has zero latency (simple array slice). SuperCompress adds ~15ms. Top-K/MMR add ~20-50ms. Headroom adds ~100ms + cold-start warmup. HyDE and Self-RAG add 500ms+ because they involve additional LLM calls.

Try it now → Read the full guide GitHub