Visual Document Retrieval benchmark designed to evaluate embedding models and retrieval systems on visually rich documents containing tables, charts, diagrams, and complex layouts. The standard benchmark for assessing multi-modal document understanding and retrieval performance.

Comprehensive benchmark suite for evaluating embedding models across 58 datasets spanning 112 languages and eight task types including retrieval, clustering, and semantic similarity, the standard for comparing embedding quality.

Massive Multilingual Text Embedding Benchmark covering over 500 quality-controlled evaluation tasks across 250+ languages, representing the largest multilingual collection of embedding model evaluation tasks.

WEAVESS is an open-source benchmarking and evaluation framework for graph-based approximate nearest neighbor (ANN) search methods, providing code and experiments for large-scale vector similarity search. It is useful for researchers and practitioners comparing vector indexing algorithms for vector databases and AI search applications.

BEIR (Benchmarking IR) is a benchmark suite for evaluating information retrieval and vector search systems across multiple tasks and datasets. Useful for comparing vector database performance.