Lightning-Fast Reranking with zerank-1

Speed is the secret ingredient that makes great AI feel instant

What is a reranker and why you need one

A reranker is a cross-encoder neural model that takes a short list of candidate documents from a fast first-stage search (BM25, vector search or hybrid) and rescoring them with full query–document context. This second-pass step dramatically boosts precision in your top-k results, ensuring your LLM or user sees the most relevant snippets first.

Benchmark results

zerank-1 is:

• ~12 % faster than Cohere 3.5 on small payloads (149.7 ms vs 171.5 ms)

• ~31 % faster on large payloads (314.4 ms vs 459.2 ms)

• 9× faster than Jina on 12 KB queries and 14× faster on 150 KB queries

All while delivering the highest NDCG@10 of the group.

Why speed matters

Whether you’re powering an enterprise search portal or a conversational voice agent, every millisecond counts. Here are some examples why:

• RAG apps: Users expect sub-second results. Slow reranking means cold leads and frustrated employees.

• Voice AI agents: Jitter in your pipeline breaks the illusion of a human-like dialogue. Quick reranking keeps the conversation flowing.

• E-commerce search bars: Users only go through the top ~10 results which need to be very accurate, but every wasted millisecond can make them churn.

When to use a reranker

• Tight LLM contexts: Surface the few most relevant documents so your prompt stays under token limits.

• Precision-critical workflows: Legal search, medical Q&A or compliance use cases where every bit of relevance matters.

• Cost-sensitive scale: Lower inference time means lower compute bills at 100 M+ monthly calls.

Try zerank-1 today

Experience sub-200 ms reranking with top-tier accuracy:

• API: integrate in minute

• Hugging Face: pull the weights and run locally

Give your search, agent or RAG pipeline the speed boost it needs.