Cohere Releases Embed 5: How It Compares to Voyage 4 Large, Gemini Embedding 2, and OpenAI
Cohere has released Embed 5, a new embedding model family. It targets enterprise search, RAG, and agentic retrieval. The model family ships in 2 tiers. Embed 5 Pro targets maximum retrieval quality. Embed 5 Fast targets latency and cost on the live query path. Both accept text, images, and fused text plus image inputs. Both cover 100+ languages and read up to 128K tokens. The key design choice: Pro and Fast share 1 embedding space. You can index with one and query with the other. Is it deplo
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Cohere has released Embed 5, a new embedding model that converts text and images into numerical vectors for search and data retrieval tasks. The model comes in two versions: a Pro tier optimized for accuracy and a Fast tier optimized for speed and cost, with both sharing the same underlying vector space so users can index with one and query with the other. Embed 5 Pro achieves higher benchmark scores than competing models from Voyage, Google Gemini, and OpenAI on standard retrieval tests, while Fast processes documents 2.4 times faster than Pro at lower cost. The model supports over 100 languages, handles inputs up to 128,000 tokens, and allows flexible output formats ranging from 256 to 2,048 dimensions, enabling significant storage savings when using lower precision representations.
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