Scan to join WeChat groupEmbed v5.0 Fast
Embed v5.0 Fast for multimodal semantic search and document indexing
Embed v5.0 Fast embeds text, images and mixed documents with 128k context. Pro prioritizes quality; Fast prioritizes lower latency. It returns vectors, not chat answers.
descriptionOverview
Overview
Embed v5.0 Fast uses model ID embed-v5.0-fast through the Embed endpoint for text, images and mixed documents such as PDFs. The official catalog lists 128k context and 256, 512, 768, 1024, 1536 or 2048 output dimensions, defaulting to 2048.
Use cases
Evaluate multimodal semantic search, document indexing, classification and RAG. Pro favors quality; Fast favors lower latency. It does not generate chat answers. Validate index compatibility when switching models. Release date and pricing are unconfirmed.
lightbulbUse cases
- Semantic search embeddings
- RAG knowledge-base indexing
- Clustering and similarity
- Cross-language retrieval
thumb_upStrengths
- Strong focus on enterprise retrieval and RAG
- Complete generation, embedding and reranking stack
- Useful for knowledge-base and search pipelines
- Multilingual support for global products
infoLimitations
- Results depend on retrieval pipeline design
- Embedding and reranking models are not chat generators
- Chinese and domain terminology need evaluation
- Limits depend on Cohere documentation