TL;DR – We’re excited to introduce voyage-3.5 and voyage-3.5-lite, the latest generation of our embedding models. These models offer improved retrieval quality over voyage-3 and voyage-3-lite at the same price, setting a new frontier for price-performance.
Both models support embeddings in 2048, 1024, 512, and 256 dimensions, with multiple quantization options enabled by Matryoshka learning and quantization-aware training. voyage-3.5 and voyage-3.5-lite outperform OpenAI-v3-large by 8.26% and 6.34%, respectively, on average across evaluated domains, with 2.2x and 6.5x lower respective costs and a 1.5x smaller embedding dimension.Compared with OpenAI-v3-large (float, 3072), voyage-3.5 (int8, 2048) and voyage-3.5-lite (int8, 2048) reduce vector database costs by 83%, while achieving higher retrieval quality. Today, we’re excited to introduce voyage-3.5 and voyage-3.5-lite, which maintain the same sizes as their predecessors—voyage-3 and voyage-3-lite—but offer improved quality for a new retrieval frontier.Matryoshka embeddings and quantization. voyage-3.5 and voyage-3.5-lite support 2048, 1024, 512, and 256 dimensional embeddings enabled by Matryoshka learning and multiple embedding quantization options—including 32-bit floating point, signed and unsigned 8-bit integer, and binary precision—while minimizing quality loss.Further, comparing OpenAI-v3-large (float, 3072) with voyage-3.5 and voyage-3.5-lite (both binary, 1024), vector database costs are reduced by 99%, with outperformance of 3.63% and 1.29% respectively. Datasets. We evaluate on 100 datasets spanning eight domains: technical documentation, code, law, finance, web reviews, multilingual, long documents, and conversations.
