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AI model category

Embedding models

The search layer behind knowledge assistants, semantic search and recommendations.

  • 6 models

Embedding models turn text into lists of numbers, called vectors, so that similar meanings end up close together. They power the search step in retrieval-augmented generation, where an assistant first finds the right passages and then answers from them. Rerank models take the top results and sort them more precisely.

Embeddings are cheap per request, but switching models later means re-embedding every document, so the choice deserves a proper test.

Embeddings

All embedding models

Embeddings

Gemini Embedding 2

Google

Google's current embedding model, multimodal: it embeds text, images, video, audio and PDFs for search and retrieval.

  • Google
  • Vision
Embeddings

text-embedding-3-large

OpenAI

OpenAI's most capable embedding model for search and retrieval, with 3,072-dimension vectors and support for English and other languages.

  • OpenAI
Embeddings

text-embedding-3-small

OpenAI

OpenAI's efficient, lowest-cost embedding model, with 1,536-dimension vectors for search, retrieval and similarity at scale.

  • OpenAI
Embeddings

Cohere Embed v4

Cohere

Cohere's multimodal embedding model for enterprise search, embedding text, images and mixed documents, with flexible vector sizes and long inputs.

  • Cohere
  • Vision
Embeddings

Cohere Rerank 4

Cohere

Cohere's multilingual rerank models, which sort search results by relevance, in a best-quality Pro version and a low-latency Fast version.

  • Cohere
Embeddings

Voyage 4

Voyage AI by MongoDB

Voyage AI's current embedding series, now part of MongoDB, with large, standard and lite models that share one embedding space.

  • Voyage AI by MongoDB

How to choose

How to choose in this category

Build a small test set of real questions with the passages that should answer them, and measure how often each model finds them. Check language support if your documents are not all in English, and the maximum input length per chunk. Adding a rerank step is often the cheapest way to improve results.

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