Embedding & Retrieval
Embedding models turn text (or other data) into vectors that can be compared for semantic similarity — the foundational layer beneath every RAG (retrieval-augmented generation) pipeline, semantic search product, and recommendation system. This category is judged on retrieval-benchmark performance (MTEB and similar), embedding dimensionality/cost tradeoffs, and how well a general-purpose embedding model transfers to a specific domain (code, legal, medical) without fine-tuning. GROUNDING tracks new embedding-model releases and their retrieval-benchmark results, since a weak embedding model silently caps the quality of every RAG system built on top of it, regardless of how good the generation model is.
At a glance
- 14 tracked models
- Most recently updated: EmbeddingGemma 2 (2026-10-06)
Most actively covered
- EmbeddingGemma 2
- BGE-M3
- BGE
- E5
- Qwen3-Embedding-0.6B
- Qwen3-Embedding-8B
- Qwen3-Embedding
- embeddinggemma
- multilingual E5
- LaBSE
FAQ
What is the Embedding & Retrieval category?
Models that produce vector embeddings for semantic search and RAG.
How many models are in this category?
GROUNDING currently tracks 14 models in Embedding & Retrieval.
How current is this hub?
The most recently updated entry is EmbeddingGemma 2, last mentioned 2026-10-06; the radar refreshes hourly.