Massive Text Embedding Benchmark leaderboard covering 58 datasets across 112 languages and 8 embedding tasks. Industry-standard benchmark for comparing text embedding models.

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.

Comprehensive benchmark for evaluating long-term memory in chat assistants with 500 manual questions testing information extraction, multi-session reasoning, and temporal reasoning across 115K-1.5M tokens.

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.

A collection of datasets curated by Intel Labs specifically for evaluating and benchmarking vector search algorithms and databases.

Benchmark results and tools by MyScale aimed at measuring the performance of vector databases in various search and retrieval tasks.