voyage-3 & voyage-3-lite: A new generation of small yet mighty general-purpose embedding models – Voyage AI
TL;DR – We are excited to announce voyage-3 and voyage-3-lite embedding models, advancing the frontier of retrieval quality, latency, and cost. voyage-3 outperforms OpenAI v3 large by 7.55% on average across all evaluated domains, including code, law, finance, multilingual, and long-context, with 2.2x lower costs and 3x smaller embedding dimension, resulting in 3x lower vectorDB costs. voyage-3-lite offers 3.82% better retrieval accuracy than OpenAI v3 large while costing 6x less and having 6x smaller embedding dimension. Both models support a 32K-token context length, 4x more than OpenAI.
In the last nine months, we have released a suite of our Voyage 2 series embedding models, including state-of-the-art general-purpose models, such as voyage-large-2, and domain-specific models, such as voyage-code-2, voyage-law-2, voyage-finance-2, and voyage-multilingual-2, all extensively trained on data from their respective domains. For example, voyage-multilingual-2 demonstrates superior retrieval quality in French, German, Japanese, Spanish, and Korean, while still providing best-in-class performance in English. We have also fine-tuned models for companies with specific use cases and data, e.g., Harvey.ai.
Now, we are thrilled to introduce our Voyage 3 series embedding models, voyage-3 and voyage-3-lite, with voyage-3-large coming in a few weeks. These models outperform competitors1 in retrieval quality while significantly reducing price and downstream costs for vectorDB. Specifically, voyage-3:
