Embedding models convert text (and sometimes images) into dense numerical vectors that capture semantic meaning, enabling similarity search, retrieval-augmented generation (RAG), and clustering. The choice of embedding model significantly impacts retrieval quality, cost, and latency. 1)
text-embedding-3-small:
text-embedding-3-large:
Both models are English-focused with limited multilingual optimization. 2)
embed-v3:
embed-v4:
BGE-M3 is the leading open-source embedding model:
E5 models range from small (384 dimensions, 118M parameters) to large (4,096 dimensions):
Jina v2: 768-1,024 dimensions, ~$0.05/M tokens via API, self-hostable
Jina v3/v4:
voyage-3-large and Voyage Multimodal 3.5:
The Massive Text Embedding Benchmark (MTEB) evaluates embedding models across retrieval, classification, clustering, and other tasks. As of 2025-2026: 8)
Top proprietary: OpenAI text-embedding-3-large, Voyage-3-large, Gemini Embedding 2
Top open-source: BGE-M3, Jina v4, llama-embed-nemotron-8b
Multilingual leaders: Cohere v4, BGE-M3 (cross-lingual R@1 > 0.98)
Efficiency leaders: E5-small (14x faster, 100% Top-5 accuracy)
No single model dominates all categories; the best choice depends on language requirements, budget, latency constraints, and whether self-hosting is needed.
Matryoshka Representation Learning (MRL) trains embeddings so that truncating dimensions preserves most of the original performance. This allows trading storage and compute for quality: 9)
| Use Case | Recommended Models |
|---|---|
| General RAG (English) | OpenAI text-embedding-3-large or -small |
| Multilingual RAG | Cohere embed-v4 or BGE-M3 |
| Cost-sensitive / Self-hosting | BGE-M3 or E5-small |
| Storage-constrained | Voyage (MRL at 512 dims) or Jina v4 |
| Multimodal (image/PDF) | Jina v4 or Voyage Multimodal 3.5 |
| Enterprise two-stage | Cohere v4 + Cohere Reranker |
Always pair embeddings with a reranker for 5-10% additional retrieval quality gains. 10)