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voyage-3-large

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.

index.ts
import { embed } from 'ai';


const result = await embed({
  model: 'voyage/voyage-3-large',
  value: 'Sunny day at the beach',
})
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Frequently Asked Questions

  • What embedding dimensions does voyage-3-large support?

    2048, 1024, 512, and 256. Matryoshka learning ensures lower-dimensional embeddings retain most of the retrieval quality of full 2048-dimensional output. Start with 1024 for most production use cases.

  • How does voyage-3-large compare to OpenAI text-embedding-3-large?

    voyage-3-large outperforms OpenAI text-embedding-3-large by 9.74% on average across 100 retrieval datasets spanning eight domains. Binary 512-dimensional voyage-3-large embeddings outperform OpenAI's full 3072-dimensional float embeddings while using 200x less storage.

  • What quantization formats does voyage-3-large support?

    32-bit float, signed 8-bit integer, unsigned 8-bit integer, and binary precision. Quantization-aware training means these reduced-precision formats lose minimal quality compared to full float precision.

  • Does voyage-3-large replace Voyage AI's domain-specific models?

    voyage-3-large outperforms Voyage AI's domain-specific models on legal and financial retrieval benchmarks. For most teams, it eliminates the need to manage separate domain-specific embedding endpoints.

  • What is the context window for voyage-3-large?

    0 tokens. This lets you embed longer documents and retrieval chunks without truncation, compared to the shorter context windows of many competing embedding models.

  • How do I authenticate voyage-3-large through Vercel AI Gateway?

    Add your Voyage AI API key in AI Gateway settings, then send embedding requests through AI Gateway using the standard embedding API format. AI Gateway authenticates requests, routes them, and records usage.

  • Can I switch embedding dimensions after indexing?

    No. Switching dimensions requires re-embedding and re-indexing your entire corpus. Choose your target dimension before building your production index. Start with 1024-dimensional int8 embeddings for a good accuracy-to-storage ratio.