Przeglądaj 7 modeli AI od VoyageAI by MongoDB i porównuj kontekst, ceny, modalności i możliwości.
7 modeli w tym widoku
VoyageAI by MongoDB
rerank-2.5 is a cutting-edge reranker optimized for quality, delivering a 7.94% improvement in retrieval accuracy over Cohere Rerank v3.5 across 93 datasets. It also outperformed Cohere Rerank v3.5 by 12.70%…
VoyageAI by MongoDB
rerank-2.5-lite is a reranker optimized for both latency and quality, delivering a 7.16% improvement in retrieval accuracy over Cohere Rerank v3.5 across 93 datasets. It also outperformed Cohere Rerank v3.5…
VoyageAI by MongoDB
voyage-4 is a general-purpose (including multilingual) embedding model optimized for retrieval/search and AI applications. voyage-4 supports embeddings in 2048, 1024, 512, and 256 dimensions, with multiple quantization options. Learn more…
VoyageAI by MongoDB
voyage-4-large is a state-of-the-art general-purpose and multilingual embedding optimized for retrieval quality. Enabled by Matryoshka learning and quantization-aware training, voyage-4-large supports embeddings in 2048, 1024, 512, and 256 dimensions, with…
VoyageAI by MongoDB
voyage-4-lite is a lightweight, general-purpose embedding model optimized for low latency and cost. Enabled by Matryoshka learning and quantization-aware training, voyage-4-lite supports embeddings in 2048, 1024, 512, and 256 dimensions,…
VoyageAI by MongoDB
voyage-code-4 is a code embedding model from Voyage AI, a MongoDB company. It is designed for coding agents and code retrieval, with Matryoshka embeddings at 2048, 1024, 512, and 256…
VoyageAI by MongoDB
voyage-multimodal-3.5 is a state-of-the-art multimodal embedding model capable of vectorizing not only text, images, and video individually, but also content that interleaves all three modalities. It delivers excellent performance for…
Fakty dotyczące modelu i endpointu dostawcy są rozdzielone. Katalogi zewnętrzne służą do odkrywania i migawek, a oficjalna dokumentacja i zweryfikowane karty modeli mają pierwszeństwo dla danych modelu.
