Voyage 4 Large
Voyage 4 Large is Voyage AI by MongoDB's Voyage 4 flagship embedding model. It uses a mixture-of-experts (MoE) architecture. Voyage AI by MongoDB 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.
- Input price
- Input $0.12, Per 1M tokens
import { embed } from 'ai';
const result = await embed({ model: 'voyage/voyage-4-large', value: 'Sunny day at the beach',})Copy link to headingProviders
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 |
|---|
| 32K | $0.12/M | 01/15/2026 |
Copy link to headingMore models by Voyage AI by MongoDB
| Model |
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| 32K | $0.06/M | — |  | 01/15/2026 | ||||||||||
| 32K | $0.02/M | — | | 01/15/2026 | ||||||||||
| 32K | $0.02/M | — | | 08/11/2025 | ||||||||||
| 32K | $0.05/M | — | | 08/11/2025 | ||||||||||
| — | $0.02/M | — | | 05/20/2025 | ||||||||||
| — | $0.18/M | — | | 01/07/2025 |
Copy link to headingAbout 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 by MongoDB 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 by MongoDB'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.
Copy link to headingWhat 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: Zero Data Retention is offered on a per-provider and model basis. See the documentation for details.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
Copy link to headingWhen to Use Voyage 4 Large
Best for
- Corpus embedding once: You want maximum document-side quality in Voyage AI by MongoDB'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
Copy link to headingConclusion
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.
Copy link to headingFrequently 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 by MongoDB's published Voyage 4 comparison.
voyage-4is the mid-sized model. Both share the same embedding space asvoyage-4-lite.How does Voyage 4 Large compare to voyage-3-large?
Voyage AI by MongoDB 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-litewhen 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 by MongoDB 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-largedocument vectors and change query models if you use asymmetric retrieval.Is Voyage 4 Large suitable for RAG applications?
Yes. Voyage AI by MongoDB 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 by MongoDB can raise accuracy while reporting serving costs about 40% lower than comparable dense models.
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