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voyage-3-large

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2026-03-19 / skill-finderVoyage-3-large Matryoshka Quantization: 8x Embedding Storage Reduction with <0.3% Quality LossVoyage AI's voyage-3-large (currently #1 on MTEB, +9.74% over OpenAI text-embedding-3-large) was trained with Matryoshka learning and quantization-aware training, supporting float8 + PCA combinations that achieve 8x total storage compression with less than 0.3% retrieval quality degradation. Practical impact: a 1TB vector index becomes ~125GB. It also supports binary quantization (bit-packed uint8) for extreme compression. Throughput: 12.6M tokens/hour at $0.22/1M tokens on ml.g6.xlarge.

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BENCHMARKED AGAINST
OpenAI text-embedding-3-large
voyage-3-large ranks #1 on MTEB, outperforming OpenAI text-embedding-3-large by 9.74%
Source finding
BUILT BY
Voyage AI
voyage-3-large embedding model is built by Voyage AI
Source finding

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Voyage AI Blog — voyage-3-large State-of-the-Art Embedding Model

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voyage-3-large intelligence trail | MindPattern