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- No. 2090Tools index
Previous survey · No. 2095 ·
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- #71 Find AI benchmarks
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- Open Source
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- TOOL
- Use case
- Model & Agent Evaluation
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- 3.4k stars
- Latest release
- 2.22.1
- Date
About
The standard benchmark for text and multimodal embedding models, covering retrieval, classification, clustering, semantic similarity and reranking rather than a single retrieval score. The 2025 MMTEB expansion extended it to a large multilingual task set, and results are published on an interactive HuggingFace leaderboard. Apache-2.0 and Python, maintained by the embeddings-benchmark organisation.
Why it made the leaderboard
Retrieval quality is set by the embedding model, and MTEB is how you choose one: it scores across retrieval, classification, clustering, similarity and reranking instead of a single number, so a model that wins on retrieval but collapses on clustering has nowhere to hide. The MMTEB expansion extends the same treatment across many languages.
Intel on MTEB
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