Embench

Embench

Embench is a browser-based retrieval lab that lets you index a corpus and compare retrieval stacks (semantic, BM25 keyword, grep, hybrid, and reranked) side-by-side with inline evaluation metrics (precision, recall, MRR). It provides embedded open-source models and a stable JSON REST contract for runs.

Embench is research software teams evaluate for research. Use this page to review pricing, integration signals, and the best alternatives before you commit.

Free API
#106 in Research (106 tools)
Added 1 month ago
Data reviewed Aug 13, 2026

Profile facts come from the vendor source. AiMatch labels unknown pricing or API details instead of estimating them.

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Quick Overview

Best for: Research

What it does

Research software for decision-makers comparing workflow fit and alternatives.

Best fit

Research

Pricing snapshot

Free from Free

Next step

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Embench

Embench is a retrieval lab for evaluating and comparing retrieval stacks on your own corpus. It allows you to index documents and run semantic, keyword (BM25), grep, hybrid, and reranked search side-by-side, showing inline metrics such as precision, recall, and MRR so you can validate performance before integrating a retrieval stack into an agent. The lab is accessible in the browser with no signup and provides a stable JSON REST contract for runs so the same harness can be handed to an agent; hosted API, saved corpora, batch jobs, and a CLI are on the project's roadmap and waitlist.

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Key Features

Side-by-side retrieval

Run semantic, BM25 keyword, grep, hybrid, and reranked search on the same corpus in a single run to compare results.

Built-in evaluation metrics

Mark expected documents and get precision, recall, and MRR shown next to every result list for immediate comparison.

Open-source embedding and reranker models

Includes MiniLM, BGE, Qwen, Stella embeddings and cross-encoder rerankers with no setup required.

Stable REST JSON contract

Every run in the lab is a plain REST call with a stable JSON contract, enabling reuse of the same harness for agents.

Browser-accessible lab

Explore and run evaluations directly in the browser; free to use and no signup required (quota per browser session).

Retrieval and indexing controls

Options shown in the UI include split/chunk methods, chunk size and overlap, Top K, hybrid weight, and similarity metric (e.g., cosine).

Pricing

Free Tier Available

Free to use in the browser with no signup; quota is limited per browser session.

Free

Free
  • Free to use in the browser
  • No signup required
  • Quota per browser session

Use Cases

Selecting a retrieval stack for an agent

Compare semantic, keyword, hybrid, and reranked approaches on your corpus and measure precision/recall/MRR to choose the best strategy before deployment.

Evaluating embedding and reranker combinations

Test multiple embedding models and cross-encoder rerankers side-by-side to see which combination retrieves the most relevant documents.

Handing a validated harness to production agents

Use the lab's stable JSON REST contract to hand the same evaluation harness to an agent or downstream system for consistent behavior.

Integrations

REST / JSON harness

Runs are plain REST calls with a stable JSON contract so the same harness can be used by agents and automation.

Agent integration

The lab is designed so results and harnesses can be handed to agents for production usage.

CLI & hosted features (roadmap)

Hosted API, saved corpora, batch jobs, and a CLI are on the way (join the waitlist).

Benefits

Direct side-by-side comparison of multiple retrieval methods on the same data.
Inline evaluation metrics (precision, recall, MRR) to quantify retrieval performance.
Quick experimentation with open-source embedding and reranker models without local setup.
No signup required and immediate browser access to test on your corpus.
Stable REST JSON contract enables reusing the same runs in agents and automation.

Limitations

Quota limitations: the browser-accessible lab is free but enforces a quota per browser session.
Hosted API, saved corpora, batch jobs, and a CLI are not yet generally available and are on the project's roadmap/waitlist.

Frequently Asked Questions

No verified FAQs are available.

Getting Started

  1. 1 Step 1: Open the Embench lab in your browser (no signup required).
  2. 2 Step 2: Index your corpus (documents one per line or in the form `doc-id | text`).
  3. 3 Step 3: Select embedding and reranker models, configure chunking/splitting, Top K, and retrieval modes, then run the evaluation.
  4. 4 Step 4: Mark expected relevant document IDs (or mark results after searching) to compute precision, recall, and MRR.
  5. 5 Step 5: Use the provided REST JSON contract for runs to hand the same harness to your agent or automation.

Support

No verified support channels are available.

API

Available: Yes

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