voyage-multilingual-2: Multilingual Embedding Model

Voyage-multilingual-2 is a newly released model optimized for multilingual retrieval and retrieval-augmented generation (RAG), outperforming alternatives such as OpenAI v3 large and Cohere multilingual v3 across major languages like French, German, Japanese, Spanish, and Korean, while maintaining strong performance in English. It excels with an average of 5.6% improvement over the second-best performing model and supports a large 32K context length, making it suitable for expertise-intensive domains including code, law, and finance. Evaluated using over 85 datasets covering 27 languages, voyage-multilingual-2 achieves superior retrieval accuracy, particularly in multilingual contexts, and is assessed using the normalized discounted cumulative gain (NDCG@10) metric. This model is designed to enhance Gen AI applications for global users and multilingual developers, providing a promising tool for those in need of advanced multilingual support.

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