embeddings-benchmark/results

MTEB: State-of-the-art evaluation of embeddings across languages and modalities

Code, datasets, and results for "The Embedder's Dilemma: LLMs Are Better, but at What Cost?" (COLM 2026). MTEB(LLM) benchmarks 10 LLMs and 26 embedding models on 37 tasks with full cost and throughput accounting.

The Universe of Evaluation. All about the evaluation for LLMs.

Cited 83-model x 49-benchmark LLM evaluation matrix with 18 matrix completion methods

State-of-the-Art Embeddings, Retrieval, and Reranking

Every Eval Ever is a shared schema and crowdsourced eval database. It defines a standardized metadata format for storing AI evaluation results — from leaderboard scrapes and research papers to local evaluation runs — so that results from different frameworks can be compared, reproduced, and reused.

Meet JevEmbed — turn embeddings into decisions. Choose, score, and judge with your choice of embedding model.

Evaluating LLMs with Dynamic Data

Text and code embeddings research from CodeFuse: C2LLM, D2LLM, E2LLM, F2LLM, ML-Embed