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Voyage 4 Large

Voyage 4 Large is Voyage AI's Voyage 4 flagship embedding model. It uses a mixture-of-experts (MoE) architecture. Voyage AI reports state-of-the-art general retrieval in their published benchmarks, with serving costs about 40% lower than comparable dense models, and average gains over OpenAI text-embedding-3-large, Cohere Embed v4, and Gemini Embedding 001 in the same comparison. It shares one embedding space with voyage-4 and voyage-4-lite.

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


const result = await embed({
  model: 'voyage/voyage-4-large',
  value: 'Sunny day at the beach',
})
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Frequently Asked Questions

  • What is the difference between Voyage 4 Large and voyage-4?

    Voyage 4 Large is the MoE flagship with the highest average scores in Voyage AI's published Voyage 4 comparison. voyage-4 is the mid-sized model. Both share the same embedding space as voyage-4-lite.

  • How does Voyage 4 Large compare to voyage-3-large?

    Voyage AI reports better retrieval accuracy than voyage-3-large at a lower price, using MoE and the Voyage 4 training stack. Moving from Voyage 3 to Voyage 4 requires re-embedding because the embedding space changes.

  • What is the context window for Voyage 4 Large?

    32K tokens. Size chunks so single requests stay under this limit on long documents.

  • When should I use Voyage 4 Large over voyage-4-lite?

    Use Voyage 4 Large when you need the strongest published Voyage 4 vectors, especially for one-time or infrequent document embedding. Use voyage-4-lite when you want fewer parameters for queries or symmetric indexing at lower compute.

  • How do I access Voyage 4 Large 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.

  • Do I need to re-embed my data to switch from voyage-3-large?

    Yes. Moving from Voyage 3 to Voyage 4 requires re-embedding because the embedding space is new. Within Voyage 4, you can often keep voyage-4-large document vectors and change query models if you use asymmetric retrieval.

  • Is Voyage 4 Large suitable for RAG applications?

    Yes. Voyage AI positions it for retrieval-augmented generation and high-accuracy document indexing, including asymmetric setups where queries use a smaller Voyage 4 model.

  • What is mixture-of-experts in Voyage 4 Large?

    Voyage 4 Large routes tokens through expert subnetworks so Voyage AI can raise accuracy while reporting serving costs about 40% lower than comparable dense models.