# Voyage 4 Lite

The lowest-cost model in Voyage AI by MongoDB's Voyage 4 generation. Voyage 4 Lite targets high-throughput embedding workloads where per-token price is the binding constraint, achieving retrieval quality near voyage-3.5 at reduced compute.

- **Model ID:** `voyage/voyage-4-lite`
- **Type:** embedding
- **Providers:** voyage
- **Context window:** 32,000
- **Maximum output tokens:** 0
- **Pricing:** $0.02/1M input tokens
- **Canonical page:** https://vercel.com/ai-gateway/models/voyage-4-lite

## Supported parameters

Detailed capability metadata has not been reported for this model.

## Example

```ts
import { embed } from 'ai';

const result = await embed({
  model: 'voyage/voyage-4-lite',
  value: 'Sunny day at the beach',
})
```

## About

Voyage 4 Lite strips down the Voyage 4 architecture to fewer parameters, released January 15, 2026. The result is a model that processes tokens faster and cheaper than its siblings while retaining enough retrieval quality for most production use cases.

Voyage AI by MongoDB benchmarks Voyage 4 Lite near voyage-3.5 retrieval accuracy. For teams running millions of daily requests or indexing large corpora on a budget, the per-token savings add up fast. Development and staging environments also benefit: cheaper iteration cycles let you experiment with chunking strategies and retrieval pipelines without burning through credits.

Because all Voyage 4 models produce compatible vectors, you aren't locked into Voyage 4 Lite for every step of your pipeline. Index your corpus with a stronger variant, then point live traffic at Voyage 4 Lite for lower query costs. No re-indexing required.

## What to consider

Use Voyage 4 Lite for queries when `voyage-4-large` already holds your document vectors, or as a budget option when both sides use the same model and your accuracy targets match Voyage AI by MongoDB's voyage-3.5 positioning. Plan a full re-embed when moving into Voyage 4, and test on a sample before indexing the full corpus.

## When to use

### Best For

- **Query embeddings** paired with `voyage-4-large` document vectors
- **Cost-sensitive symmetric** use when voyage-3.5-level accuracy is enough
- **High query volume** and early production

### Consider Alternatives When

- **You need higher published scores**; use `voyage-4-large` or `voyage-4`
- **Code-only corpora**; use `voyage-code-3`
- **Multimodal embeddings**; use a model with native image inputs

## Best for

- **High query traffic:** Pair Voyage 4 Lite queries with `voyage-4-large` document embeddings to keep per-query cost low without re-indexing
- **Cost-sensitive symmetric indexing:** Voyage 4 Lite on both sides when voyage-3.5-level retrieval accuracy is sufficient and per-token cost drives the decision
- **Early production and prototypes:** Iterate cheaply before upgrading query-side models once traffic patterns stabilize
- **Batch jobs:** Large-corpus indexing runs where per-token cost compounds across millions of requests

## Consider alternatives

- **Higher published average scores:** Use `voyage-4-large` or `voyage-4` when retrieval accuracy matters more than per-token cost
- **Code-only corpora:** Use `voyage-code-3` for repositories where source code is the primary content type
- **Multimodal embeddings:** Use a model with native image inputs when you need to embed diagrams, screenshots, or mixed-format documents

## Frequently asked questions

### What is the difference between Voyage 4 Lite and voyage-4?

`voyage-4` is the mid-sized Voyage 4 model; Voyage AI by MongoDB reports it approaches voyage-3-large quality. Voyage 4 Lite uses fewer parameters; Voyage AI by MongoDB reports it approaches voyage-3.5 accuracy. Both share the same embedding space as `voyage-4-large`.

### How does Voyage 4 Lite compare to voyage-3.5-lite?

Voyage 4 Lite is a Voyage 4 model with a shared embedding space and updated training. Voyage AI by MongoDB positions it near voyage-3.5 accuracy with fewer parameters. Moving from Voyage 3.x requires re-embedding.

### What is the context window for Voyage 4 Lite?

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

### Is Voyage 4 Lite suitable for production use?

Yes. Voyage AI by MongoDB targets production for `voyage-4-lite`, including asymmetric setups with `voyage-4-large` document embeddings when query cost matters.

### How do I access Voyage 4 Lite 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.

### When should I move from Voyage 4 Lite to voyage-4 or voyage-4-large for queries?

Move when you need higher query-side accuracy. If documents stay on `voyage-4-large`, you can upgrade query embeddings to `voyage-4` or `voyage-4-large` without re-vectorizing documents in that asymmetric setup.

### Do I need to re-embed my data to switch from voyage-3.5-lite?

Yes. Voyage 3 and Voyage 4 use different embedding spaces, so you re-embed when you change generations.

## Links

- [Model page](https://vercel.com/ai-gateway/models/voyage-4-lite)
- [AI Gateway documentation](https://vercel.com/docs/ai-gateway)
- [Provider model documentation](https://docs.voyageai.com/docs/embeddings)
- [Provider pricing](https://docs.voyageai.com/docs/pricing)