voyage-3.5 and voyage-3.5-lite: improved quality for a new retrieval frontier

Voyage-3.5 and voyage-3.5-lite are new embedding models that improve retrieval quality over their predecessors, voyage-3 and voyage-3-lite, while maintaining the same price and size. These models offer embeddings in various dimensions and utilize Matryoshka learning and quantization-aware training to enhance performance. They outperform the OpenAI-v3-large model in multiple domains, reducing vector database costs significantly with their smaller embedding dimensions and quantization options. Evaluations across 100 datasets in domains such as technical documentation, code, law, finance, and multilingual content show that voyage-3.5 and voyage-3.5-lite achieve superior retrieval quality, demonstrating state-of-the-art cost-performance ratios. The models introduce a binary rescoring method that further enhances retrieval quality while minimizing database costs. Both models are currently available, with the first 200 million tokens offered for free.