EmbeddingGemma 2 is a multimodal embedding model from Google built on the Gemma 4 architecture.
Laya is a 421M decision model from Convai Innovations, fine-tuned from ModernBERT-large.
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
BGE-M3 is a new model from BAAI distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity.
EmbeddingGemma is a 300M parameter embedding model from Google.
Embedding models on very large sentence level datasets.
A suite of text embedding models by Snowflake, optimized for performance.
Sentence-transformers model that can be used for tasks like clustering or semantic search.
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.