# Embedding Models

Embedding models convert text into dense vector representations, enabling semantic search, clustering, and similarity comparison. They are essential for building search engines, recommendation systems, and knowledge retrieval applications.

* [<mark style="color:blue;">**BGE Large 1.5**</mark>](/cloud/ai/ai-model-hub/models/embedding-models/bge-large-1-5.md): Maps English text to a 1024-dimensional dense vector space, supporting high-quality semantic search and retrieval tasks.
* [<mark style="color:blue;">**BGE m3**</mark>](/cloud/ai/ai-model-hub/models/embedding-models/bge-m3.md): Handles long multilingual text and maps it to a 1024-dimensional vector space for cross-lingual search and analysis.
* [<mark style="color:blue;">**Paraphrase Multilingual MPNet v2**</mark>](/cloud/ai/ai-model-hub/models/embedding-models/paraphrase-multilingual-mpnet-v2.md): A sentence transformer model that maps text to a 768-dimensional vector space, enabling efficient paraphrase detection and semantic similarity tasks.
* [<mark style="color:blue;">**Qwen3 VL Embedding 8B**</mark>](/cloud/ai/ai-model-hub/models/embedding-models/qwen3-vl-embedding-8b.md): A multimodal embedding model that maps text and images to a 4096-dimensional vector space, enabling cross-modal semantic search and visual document retrieval across 30+ languages.


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