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.
import { embed } from 'ai';
const result = await embed({ model: 'voyage/voyage-4-large', value: 'Sunny day at the beach',})Providers
Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.
| Provider |
|---|
 Voyage AI | 32K | $0.12/M | — | — | 01/15/2026 |
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| 32K | $0.06/M | — | — | | 01/15/2026 | ||||||||
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| 32K | $0.05/M | — | — | | 08/11/2025 | ||||||||
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| $0.06/M | — | — | | 05/20/2025 |
About Voyage 4 Large
Voyage 4 Large is the first production embedding model to use a mixture-of-experts architecture, released January 15, 2026. MoE activates only a subset of parameters per token, achieving flagship-level retrieval accuracy at lower inference cost than a dense model of equivalent quality.
Voyage AI reports Voyage 4 Large surpasses voyage-3-large on retrieval accuracy at a lower price point, with serving costs about 40% below comparable dense models. It supports a context window of 32K tokens and the full Matryoshka dimension set (2048, 1024, 512, 256) with quantization-aware training.
As the top of the Voyage 4 series, Voyage 4 Large produces the strongest average retrieval scores in Voyage AI's published benchmarks. Use it for document embeddings in asymmetric setups where you pair it with voyage-4 or voyage-4-lite on the query side to control per-query costs.
What To Consider When Choosing a Provider
- Configuration: Voyage 4 Large targets teams that need top published scores and can pay for the flagship on the paths that matter (often document embedding).
- Configuration: Pair Voyage 4 Large document vectors with smaller Voyage 4 query models when query volume is high.
- Configuration: Treat a move to Voyage 4 as a new index. Test on a sample corpus before you re-embed everything.
- Zero Data Retention: AI Gateway does not currently support Zero Data Retention for this model. See the documentation for models that support ZDR.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
When to Use Voyage 4 Large
Best For
- Corpus embedding once: You want maximum document-side quality in Voyage AI's published Voyage 4 results
- Asymmetric RAG: Documents use Voyage 4 Large and queries use
voyage-4-lite - Enterprise search: Long documents within the window of 32K tokens
- Upgrades from voyage-3-large: You accept a full re-embed for Voyage 4's shared space and MoE gains
Consider Alternatives When
- Lower per-query cost: Use
voyage-4orvoyage-4-litefor queries, or symmetric indexing with a smaller Voyage 4 model - Mid-tier symmetric use:
voyage-4when you want one model for both sides - Code-only corpora: Use
voyage-code-3for repositories where source code is the primary content type - Multimodal embeddings: Pick a model with native image inputs
Conclusion
MoE architecture gives Voyage 4 Large flagship retrieval accuracy at lower serving costs than dense alternatives. Use it for document embeddings and pair with lighter Voyage 4 models on queries to optimize per-request spend through AI Gateway.