Qwen3 Embedding

A versatile multilingual text embedding model from BAAI that supports 100+ languages and can handle inputs up to 8192 tokens. BGE-M3 is unique in supporting three retrieval methods simultaneously: dense retrieval, multi-vector retrieval, and sparse retrieval.

A state-of-the-art multilingual text embedding model from Alibaba's GTE (General Text Embedding) series, built on the Qwen2-1.5B LLM. The model supports up to 8192 tokens and incorporates bidirectional attention mechanisms for enhanced contextual understanding across diverse domains.

Suite of high-quality multilingual text embedding models optimized for retrieval performance, developed by Snowflake and available as open-source for commercial use.

Open-source retrieval and RAG framework from BAAI featuring the BGE embedding model series. BGE-M3 supports multi-functionality (dense, sparse, multi-vector), multi-linguality (100+ languages), and multi-granularity (up to 8192 tokens).

Jina AI's latest embedding model achieving the highest multilingual performance among models under 1B parameters with 71.7 average MTEB score and 67.7 MMTEB score.

Open-source multilingual embedding model using Mixture-of-Experts architecture, achieving excellent semantic performance with efficient inference and full offline support.