Embedding models

Developer documentation

Vector generation for semantic search, RAG, retrieval, clustering, ranking, and analytics.

Model Reference

Embedding models

Vector generation for semantic search, RAG, retrieval, clustering, ranking, and analytics. Endpoint: http://omixa.cloud/api/v1/embeddings

Cohere Embed v4.0

embed-v-4-0

Cohere Embed v4.0 for embeddings, retrieval, reranking, or vector analytics.

Embedding Context window: 512 tokens

input per 1m tokens $0.120000

minimum hold $0.010000

Cohere Rerank v4.0 Fast

Cohere-rerank-v4.0-fast

Cohere Rerank v4.0 Fast for embeddings, retrieval, reranking, or vector analytics.

Embedding

minimum hold $0.010000

Cohere Rerank v4.0 Pro

Cohere-rerank-v4.0-pro

Cohere Rerank v4.0 Pro for embeddings, retrieval, reranking, or vector analytics.

Embedding

minimum hold $0.010000

Embedding 001

embedding-001

Embedding 001 for semantic search, retrieval, ranking, and vector analytics.

Embedding Context window: 2,048 tokens

input per 1m tokens $0.150000

minimum hold $0.010000

Gemini Embedding

gemini-embedding-001

Gemini Embedding for semantic search, retrieval, ranking, and vector analytics.

Embedding Context window: 8,192 tokens

input per 1m tokens $0.150000

minimum hold $0.010000

Gemini Embedding 2

gemini-embedding-2

Gemini Embedding 2 for semantic search, retrieval, ranking, and vector analytics.

Embedding Context window: 8,192 tokens

input per 1m tokens $0.200000

minimum hold $0.010000

Text Embedding 004

text-embedding-004

Text Embedding 004 for semantic search, retrieval, ranking, and vector analytics.

Embedding Context window: 2,048 tokens

input per 1m tokens $0.150000

minimum hold $0.010000

Text Embedding 3 Large

text-embedding-3-large

Text Embedding 3 Large for semantic search, retrieval, ranking, and vector analytics.

Embedding Context window: 8,191 tokens

input per 1m tokens $0.143000

minimum hold $0.010000

Text Embedding 3 Small

text-embedding-3-small

Text Embedding 3 Small for semantic search, retrieval, ranking, and vector analytics.

Embedding Context window: 8,191 tokens

input per 1m tokens $0.022000

minimum hold $0.010000

Text Embedding Ada 002

text-embedding-ada-002

Text Embedding Ada 002 for semantic search, retrieval, ranking, and vector analytics.

Embedding Context window: 8,192 tokens

input per 1m tokens $0.110000

minimum hold $0.010000