Embench
playground for comparing embeddings and retrieval stacks
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What is Embench?
Embench is a retrieval lab that lets you test multiple search methods (semantic, keyword, hybrid, reranked) on the same corpus side-by-side, with built-in evaluation metrics. It's designed for developers and AI teams to benchmark which retrieval stack works best for their data before integrating it into production agents.
Embench pricing
Pricing model: Freemium
Free browser lab with no signup required; hosted API and additional features coming soon (pricing not yet specified)
Embench pros
- Side-by-side comparison of five retrieval methods (semantic, BM25, grep, hybrid, reranked) in one run
- Built-in evaluation metrics (precision, recall, MRR) displayed inline with results
- Open-source embeddings and rerankers (MiniLM, BGE, Qwen, Stella) with zero setup required
- Free browser-based lab with no signup; can test on your own data
- REST API with stable JSON contract for seamless agent integration
Embench cons
- CLI for local and CI evaluations is still on the roadmap, not yet available
- Hosted API, saved corpora, and batch jobs are announced but not yet launched
- Free tier limited to per-browser-session quota
Frequently asked questions about Embench
What retrieval methods can I test?
Embench supports semantic search, BM25 keyword search, grep, hybrid search, and semantic search with reranking—all on the same corpus in a single run.
Do I need to set up embeddings models?
No. Embench provides open-source models like MiniLM, BGE, Qwen, and Stella embeddings plus cross-encoder rerankers with zero setup.
How do I integrate Embench into my agent?
After testing in the browser, use the REST API (documented at api.embench.ai/docs) with a stable JSON contract to make the same retrieval calls in production.
What metrics does Embench show?
Precision, recall, and MRR (mean reciprocal rank) are calculated inline when you mark expected relevant documents.