Embeddings
Semantic search and vector representations for AI applications
Embedding models for semantic search, RAG, and clustering
Embedding models turn text — or sometimes images, code, or audio — into a fixed-length vector of floating-point numbers. Similar inputs land close together in the embedding space, dissimilar inputs land far apart. Reach for embeddings when building semantic search, retrieval-augmented generation (RAG), recommendations, or clustering.
7 models available
Text Embedding 3 Large
OpenAI's most powerful embedding model. 3072 dimensions for maximum accuracy.
Voyage AI voyage-3
Voyage's general-purpose embedding model. 1024 dims, 32k context, strong retrieval performance.
Cohere embed-multilingual-v3
Cohere's multilingual embedding model. Supports 100+ languages with separate search and classification modes.
Jina Embeddings v3 (Multilingual)
Jina's frontier multilingual embedding model. 570M params, 8192 ctx, 89 languages, Matryoshka dims 128-1024.
mxbai-embed-large-v1
Mixedbread's open-source 335M embedding model. Top MTEB benchmark for English retrieval at release.
Text Embedding 3 Small
OpenAI's compact embedding model. 1536 dimensions, great for semantic search and RAG.
Voyage AI voyage-code-3
Voyage's code-specialized embedding model. Up to 32k context, Matryoshka 256-2048 dims, int8/binary support.
Top embeddings picks
Hand-picked across four common criteria — resolved against the live catalog so the picks track price and performance changes.
OpenAI's most powerful embedding model. 3072 dimensions for maximum accuracy.
Learn moreJina's frontier multilingual embedding model. 570M params, 8192 ctx, 89 languages, Matryoshka dims 128-1024.
Learn moreVoyage's general-purpose embedding model. 1024 dims, 32k context, strong retrieval performance.
Learn moreOpenAI's compact embedding model. 1536 dimensions, great for semantic search and RAG.
Learn morePricing is per-token, similar to text generation but typically 10-100× cheaper. Flagship models (OpenAI text-embedding-3-large, Voyage 3, Cohere Embed v3) cost €0.05-€0.15 per million tokens. Open-weights options (Jina V3, BGE, MxBai) cost effectively nothing to run on your own infrastructure. A typical RAG corpus of 10 million tokens (around 20,000 documents) costs €0.50-€1.50 to embed once. Re-embedding on every model upgrade is the main long-tail cost.
The trade-off is dimension, recall, and price. Higher-dimensional embeddings (3,072 or 4,096 dims) capture more nuance but cost more to store and search. Lower-dimensional models (256-768 dims) cost ten times less and still recover the right document 90-95% of the time on most workloads. Use the high-dim flagship when retrieval quality is mission-critical (legal search, medical Q&A); use a budget model when you can tolerate the occasional missed result.
Watch out for chunk size: most embedding models perform best on chunks of 200-500 tokens. Embed an entire 50-page document as one vector and you lose the per-section meaning. Embed too small (under 50 tokens) and individual chunks become noisy. Pick a chunker that respects paragraph boundaries and adds a small overlap (10-20%) between chunks.
Watch out for multilingual mismatch: not every embedding model speaks every language equally. If your corpus is multilingual, pick a model whose training data covers your languages — Jina V3, Cohere Multilingual, and Voyage Multilingual are the safe defaults.
Top picks above cover the highest-recall flagship, the cheapest production model, the highest-dimensional option, and the fastest indexer.
Popular use cases
Common patterns built with embeddings on Railwail.
Related comparisons
Side-by-side reviews of the most-compared models in this category.
Frequently asked questions
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