Voyage 4
Voyage 4 is a mid-sized model in Voyage AI by MongoDB's Voyage 4 family. Voyage AI by MongoDB reports it approaches voyage-3-large retrieval quality with a context window of 32K tokens, Matryoshka dimensions (2048, 1024, 512, 256), and multiple quantization options. All Voyage 4 models share one embedding space, so you can mix models for asymmetric retrieval.
- Input price
- Input $0.06, Per 1M tokens
import { embed } from 'ai';
const result = await embed({ model: 'voyage/voyage-4', 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 | $0.06/M | 01/15/2026 |
Copy link to headingMore models by Voyage AI by MongoDB
| Model |
|---|
| 32K | $0.12/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
Voyage 4 sits at the center of Voyage AI by MongoDB's Voyage 4 lineup, released January 15, 2026. It supports a context window of 32K tokens and occupies the middle ground between the MoE flagship voyage-4-large and the budget-oriented voyage-4-lite.
All Voyage 4 models share one embedding space. You can embed documents with voyage-4-large and run queries through Voyage 4 without maintaining separate vector indices. This asymmetric pattern lets you optimize cost per query while keeping document embeddings at flagship quality.
Voyage 4 supports Matryoshka dimensions (2048, 1024, 512, 256) and quantization-aware training across float32, int8, and binary formats. These compression options apply the same way across all Voyage 4 models, so you can tune storage costs independently of which model you choose for embedding.
Copy link to headingWhat To Consider When Choosing a Provider
- Configuration: When query volume dominates cost, embed the corpus once with
voyage-4-largeand serve queries withvoyage-4-lite, or Voyage 4. Voyage AI by MongoDB reports higher accuracy than symmetric retrieval with smaller models alone. - Configuration: Use Voyage 4 for both queries and documents when you want one model and balanced cost.
- Configuration: Moving from voyage-3.5, voyage-3-large, or older models requires re-embedding because the embedding space differs from Voyage 4.
- 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
Best for
- General-purpose retrieval: You want Voyage 4's shared space and mid-sized efficiency
- Asymmetric setups: Documents use
voyage-4-largeand queries use Voyage 4 to control latency and cost - RAG pipelines: Use Matryoshka dimensions and quantization to cut vector database cost
- Teams moving from voyage-3-large: Want Voyage 4 compatibility and flagship accuracy on the document side
Consider alternatives when
- Highest average retrieval scores in Voyage AI by MongoDB's published Voyage 4 benchmarks: Use
voyage-4-large(MoE flagship) - Lowest compute for queries: Use
voyage-4-litewhen per-query cost is the binding constraint and voyage-3.5-level accuracy is sufficient - Source-code-only corpora:
voyage-code-3stays purpose-built for code - Multimodal text-and-image embeddings: Pick a model with native image inputs
Copy link to headingConclusion
Voyage 4 is the practical default for teams that want Voyage 4 quality without flagship compute costs. The shared embedding space means you can start here and layer in voyage-4-large for documents or voyage-4-lite for high-volume queries without re-indexing.
Copy link to headingFrequently Asked Questions
What is the difference between Voyage 4, voyage-4-large, and voyage-4-lite?
voyage-4-largeis the MoE flagship with the highest average retrieval scores in Voyage AI by MongoDB's published Voyage 4 benchmarks. Voyage 4 is the mid-sized model; Voyage AI by MongoDB reports it approaches voyage-3-large quality.voyage-4-liteuses fewer parameters; Voyage AI by MongoDB reports it approaches voyage-3.5 retrieval accuracy. All three share one embedding space.How does Voyage 4 compare to voyage-3.5?
Voyage 4 is a Voyage 4 model with a shared embedding space and updated training. Voyage AI by MongoDB positions
voyage-4-litenear voyage-3.5 accuracy; Voyage 4 targets voyage-3-large-level quality. Moving from Voyage 3.x requires re-embedding your corpus.What is the context window for Voyage 4?
32K tokens. Set chunk sizes so single-pass embeds stay under this limit on long texts.
Can I use Voyage 4 for RAG applications?
Yes. Voyage 4 is a text embedding model for semantic search and retrieval-augmented generation across mixed content types, including technical documentation, business text, and conversational text.
How do I access Voyage 4 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.x to Voyage 4?
Yes. Voyage 3 and Voyage 4 use different embedding spaces, so you re-embed and re-index when you move generations. Within Voyage 4, you can often change query models without re-vectorizing documents if you follow Voyage AI by MongoDB's asymmetric retrieval pattern with
voyage-4-largedocument embeddings.What is shared embedding space in Voyage 4?
All Voyage 4 models map text into the same vector space, so embeddings from different models in the family are compatible. You can search document vectors from
voyage-4-largewith query vectors from Voyage 4 orvoyage-4-lite.
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