📢 ICYMI: We recently released the Voyage 4 model family.
Voyage 4 features an industry-first shared embedding space, eliminating the need to re-index your data when switching between models in the series.
This breakthrough is designed to solve the trade-off between frontier-level accuracy and production efficiency.
The shared embedding space enables asymmetric retrieval, for example, embedding documents with voyage-4-large for high accuracy while embedding queries with a smaller model to keep per-query latency and cost low.
Asymmetric retrieval is denoted in the figure with a *
voyage-4-large sets a new accuracy standard, outperforming voyage-4, voyage-4-lite, Gemini Embedding 001, Cohere Embed v4, and OpenAI v3 Large by an average of 1.87%, 4.80%, 3.87%, 8.20%, and 14.05%, respectively.
The first 200 million tokens are free. Learn more in our release blog: https://t.co/6HeupDGb6t
embedding — A list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.
Why it matters
A shared embeddingA list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.Full definition → space lets you embed documents with the large model and queries with a cheap one, and swap model sizes later without re-indexing the corpus.
