voyage-3-large is Voyage AI's general-purpose embedding model with a context window of 0 tokens, Matryoshka dimensionality (2048/1024/512/256), and quantization-aware training. It outperforms OpenAI text-embedding-3-large by 9.74% across 100 retrieval datasets.
import { embed } from 'ai';
const result = await embed({ model: 'voyage/voyage-3-large', value: 'Sunny day at the beach',})voyage-3-large is Voyage AI's general-purpose embedding model, released September 1, 2024. It supports a context window of 0 tokens and produces embeddings in four dimensions: 2048, 1024, 512, and 256 through Matryoshka learning. You can tune the tradeoff between retrieval accuracy and vector storage cost without retraining or running multiple models. Across 100 datasets spanning eight domains, voyage-3-large outperforms OpenAI text-embedding-3-large by 9.74% and Cohere Embed v3 English by 20.71%.
Quantization-aware training enables multiple precision formats: 32-bit float, signed and unsigned 8-bit integer, and binary. Binary 512-dimensional embeddings outperform OpenAI text-embedding-3-large at full 3072-dimensional float precision while requiring 200x less storage. Int8 precision at 1024 dimensions loses only 0.31% quality versus full-precision 2048-dimensional output, cutting storage by 8x. These options make voyage-3-large practical for large-scale production indices.
Voyage AI evaluates voyage-3-large across technical documentation, code, legal, financial, web, multilingual, long-document, and conversational domains. It outperforms Voyage AI's own domain-specific models on legal and financial retrieval tasks. That makes it a practical single-model choice when you need broad domain coverage without managing multiple specialized embedding endpoints.