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Voyage 3.5 Lite

Voyage 3.5 Lite is Voyage AI's cost-efficient embedding model with a context window of 0 tokens. It outperforms OpenAI text-embedding-3-large by 6.34% and achieves retrieval quality within 0.3% of Cohere Embed v4 at one-sixth the cost.

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


const result = await embed({
  model: 'voyage/voyage-3.5-lite',
  value: 'Sunny day at the beach',
})
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Frequently Asked Questions

  • How does Voyage 3.5 Lite compare to voyage-3.5?

    Voyage 3.5 Lite costs one-third the price of voyage-3.5. It achieves retrieval quality within 0.3% of Cohere Embed v4, while voyage-3.5 surpasses Cohere Embed v4 by 1.63%. For most production workloads, the accuracy difference is small relative to the cost savings.

  • What embedding dimensions does Voyage 3.5 Lite support?

    Four dimensions: 2048, 1024, 512, and 256. The same Matryoshka dimensionality as voyage-3.5, with the same quantization options for further storage savings.

  • Is Voyage 3.5 Lite suitable for production use?

    Yes. Voyage AI recommends it for cost-sensitive production deployments. It outperforms OpenAI text-embedding-3-large by 6.34% across eight retrieval domains.

  • What quantization formats does Voyage 3.5 Lite support?

    32-bit float, 8-bit integer, and binary precision. Binary rescoring yields up to 6.89% quality improvement, making aggressive compression practical at scale.

  • How much cheaper is Voyage 3.5 Lite than competitors?

    Voyage 3.5 Lite achieves retrieval quality within 0.3% of Cohere Embed v4 at one-sixth the cost. It also outperforms OpenAI text-embedding-3-large while maintaining a lower per-token price.

  • How do I route Voyage 3.5 Lite through Vercel AI Gateway?

    Add your Voyage AI API key in AI Gateway settings, then send embedding requests through AI Gateway. AI Gateway authenticates requests and records usage across embedding providers.

  • Can I migrate from voyage-3-lite to Voyage 3.5 Lite?

    Yes. Voyage 3.5 Lite outperforms voyage-3-lite by 4.28% on average. Migration requires re-embedding your corpus since the model weights are different.