Compare text and multimodal embedding models from Jina, Qwen, Google, OpenAI and more
Universal embedding model for multimodal and multilingual retrieval. Supports both single-vector and...
Multilingual embeddings with task-specific LoRA adapters. Optimized for text retrieval, semantic mat...
State-of-the-art multilingual text embedding model. #1 on MTEB multilingual leaderboard. Supports 10...
Google's text embedding model for semantic search, classification, and clustering. Supports flexible...
SOTA BERT-large sized embedding model. Outperforms OpenAI text-embedding-3-large and matches models ...
Multimodal embedding model for text, images, and PDFs. Supports flexible dimensions and 128K context...
English-optimized text and image embedding model. Fast and accurate for classification and retrieval...
Multilingual embedding model supporting 100+ languages. Ideal for cross-lingual retrieval and classi...
State-of-the-art general-purpose and multilingual embedding. Outperforms OpenAI-v3-large by 9.74% ac...
Top-ranked embedding model on MTEB English and Chinese. Built on Qwen2-7B with bidirectional attenti...
Top retrieval model on MTEB, ranking #1 with 60.2 score. Built on Mistral-7B with advanced data refi...
Late interaction retriever with excellent multilingual and cross-lingual performance. Fast inference...