20+ models
EmbeddingGemma 2 is a multimodal embedding model from Google built on the Gemma 4 architecture.
EmbeddingGemma is a 300M parameter embedding model from Google.
nomic-embed-text-v2-moe is a multilingual MoE text embedding model that excels at multilingual retrieval.
A high-performing open embedding model with a large token context window.
Building upon the foundational models of the Qwen3 series, Qwen3 Embedding provides a comprehensive range of text embeddings models in various sizes
State-of-the-art large embedding model from mixedbread.ai
Embedding models on very large sentence level datasets.
A suite of text embedding models by Snowflake, optimized for performance.
Snowflake's frontier embedding model. Arctic Embed 2.0 adds multilingual support without sacrificing English performance or scalability.
The IBM Granite Embedding 30M and 278M models models are text-only dense biencoder embedding models, with 30M available in English only and 278M serving multilingual use cases.
Embedding model from BAAI mapping texts to vectors.
EXAONE 3.5 is a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released by LG AI Research.
Octen-Embedding-8B, #1 on the RTEB leaderboard. 4096-dim multilingual embeddings fine-tuned from Qwen3-Embedding-8B for retrieval (legal, finance, healthcare, code).
WeMM-Embedding-2B is a universal multimodal embedding model built on Qwen3.5.
Embedding Model embeddinggemma-300M-NVFP4-Q8-GGUF
Qwen embedding model