Embedding Model

Full definition

Modern text embedding models are typically encoder-only transformers fine-tuned with contrastive learning on (anchor, positive, hard-negative) triplets. Widely used text embedding models include OpenAI text-embedding-3-large, Cohere Embed, Voyage AI, BGE, E5 and Nomic Embed. MTEB rankings change often: in June 2025 Alibaba's Qwen3-Embedding-8B ranked first on the multilingual leaderboard (70.58), ahead of Google's Gemini Embedding (68.37); Cohere's multilingual v3 scored 61.12 and OpenAI's text-embedding-3-large 58.93. Dimensionality ranges from 384 to 4096; higher dimensions cost more storage and compute but may improve retrieval. Domain fine-tuning often beats a stronger general model.

Why it matters

Embedding-model choice is the single biggest lever on RAG quality before re-ranking. Buyers should run a held-out evaluation on their own domain — public benchmarks (MTEB) are weakly correlated with enterprise-document retrieval performance.

Example

A SaaS company tests 6 embedding models on 500 internal queries; voyage-3 wins on recall@10 by 8 points over text-embedding-3-large and reduces re-ranker workload by 40%.

Next steps

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Related terms

• Embedding
  An embedding is a dense numerical vector — typically 384 to 4096 dimensions — that represents the semantic meaning of a piece of text, image, audio, or other content, so that semantically similar items end up near each other in vector space.
• Masked Language Model
  A masked language model (MLM) is a model trained to predict tokens that have been hidden in the input, learning bidirectional context rather than left-to-right next-token prediction.
• Semantic Search
  Semantic search is information retrieval that ranks documents by meaning rather than exact-keyword overlap, using vector embeddings of query and documents to measure similarity in a learned semantic space.
• Vector Database
  A vector database is a database optimized for storing and searching high-dimensional vectors (embeddings) by similarity rather than by exact match — the storage layer of every RAG and semantic-search system.

More deployment & operations terms

• Hybrid Search
  Hybrid search combines lexical retrieval (BM25 or sparse vectors like SPLADE) with dense vector retrieval and fuses the results, capturing both exact-match precision and semantic recall.
• Re-Ranker
  A re-ranker is a second-stage retrieval model that re-scores a candidate set (typically top 50–100) from a fast first-stage retriever using a more expensive cross-encoder that jointly attends to query and document.