# Voyage 3.5 Lite

Voyage 3.5 Lite is Voyage AI by MongoDB's cost-efficient embedding model. It outperforms OpenAI text-embedding-3-large by 6.34% and matches Cohere Embed v4 quality at one-sixth the cost, with a context window of 0 tokens.

- **Model ID:** `voyage/voyage-3.5-lite`
- **Type:** embedding
- **Providers:** voyage
- **Context window:** Not applicable
- **Maximum output tokens:** 0
- **Pricing:** $0.02/1M input tokens
- **Canonical page:** https://vercel.com/ai-gateway/models/voyage-3.5-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-3.5-lite',
  value: 'Sunny day at the beach',
})
```

## About

Voyage 3.5 Lite is Voyage AI by MongoDB's cost-efficient embedding model, released May 20, 2025. It supports a context window of 0 tokens and produces embeddings in four dimensions: 2048, 1024, 512, and 256. Voyage 3.5 Lite outperforms OpenAI text-embedding-3-large by 6.34% and its predecessor voyage-3-lite by 4.28% on average across eight retrieval domains.

Voyage 3.5 Lite achieves retrieval quality within 0.3% of Cohere Embed v4 at one-sixth the cost. That makes it a practical choice for high-volume embedding workloads where per-token pricing matters. It supports the same Matryoshka dimensionality and quantization-aware training as the premium voyage-3.5, including 32-bit float, 8-bit integer, and binary precision formats. Binary rescoring yields up to 6.89% quality improvement.

If you run large-scale RAG pipelines or semantic search over millions of documents, Voyage 3.5 Lite keeps embedding infrastructure affordable without dropping to a lower quality tier. Voyage AI by MongoDB recommends it for cost-sensitive production deployments.

## What to consider

Voyage 3.5 Lite costs one-third the price of voyage-3.5 while maintaining high retrieval quality. The accuracy gap is small enough that most production workloads will not see a meaningful difference in end-user outcomes.

If you embed millions of documents or process high query volumes, the per-token savings compound significantly. Voyage 3.5 Lite is designed for this scale.

Voyage 3.5 Lite supports the same dimensionality and precision options as voyage-3.5. Combine reduced dimensions with int8 or binary precision for maximum cost savings on very large indices.

## When to use

### Best For

- **High-volume RAG pipelines** where per-token embedding cost scales linearly with corpus size and query volume
- **Semantic search** over large document collections where retrieval accuracy at a fraction of the cost is the goal
- **Startup and growth-stage teams** that need production-quality embeddings without enterprise-tier pricing
- **Prototyping and experimentation** where lower cost enables faster iteration on retrieval strategies
- **Multi-domain retrieval** across the same eight domains as voyage-3.5 at a lower price point

### Consider Alternatives When

- **Retrieval accuracy is the main priority** and cost is secondary; voyage-3.5 delivers the highest retrieval accuracy among Voyage 3.5 general-purpose options
- **Your corpus is exclusively source code** and you need specialized code retrieval; voyage-code-3 is purpose-built for that domain
- **You need multimodal embeddings** that handle images and screenshots alongside text
- **You require the absolute lowest latency** per query and a smaller, faster model would better fit your SLA

## Best for

- **High-volume RAG pipelines:** Per-token embedding cost scales linearly with corpus size and query volume
- **Semantic search:** Retrieval within 0.3% of Cohere Embed v4 at one-sixth the cost across large document collections
- **Startup and growth-stage teams:** Production-quality embeddings without enterprise-tier pricing
- **Prototyping and experimentation:** Lower cost enables faster iteration on retrieval strategies
- **Multi-domain retrieval:** The same eight domains as voyage-3.5 at a lower price point

## Consider alternatives

- **Retrieval accuracy is the main priority:** voyage-3.5 delivers the highest retrieval accuracy among Voyage 3.5 general-purpose options
- **Your corpus is exclusively source code:** voyage-code-3 is purpose-built for that domain
- **You need multimodal embeddings:** voyage-3.5-lite is text-only; pick a model with native image inputs when screenshots or diagrams go into the same index
- **You require the absolute lowest latency:** A smaller, faster model better fits tight SLAs

## Frequently asked questions

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

Voyage 3.5 Lite costs one-third the price of voyage-3.5. It achieves retrieval quality within 0.3% of Cohere Embed v4, while voyage-3.5 surpasses Cohere Embed v4 by 1.63%. For most production workloads, the accuracy difference is small relative to the cost savings.

### What embedding dimensions does Voyage 3.5 Lite support?

Four dimensions: 2048, 1024, 512, and 256. The same Matryoshka dimensionality as voyage-3.5, with the same quantization options for further storage savings.

### Is Voyage 3.5 Lite suitable for production use?

Yes. Voyage AI by MongoDB recommends it for cost-sensitive production deployments. It outperforms OpenAI text-embedding-3-large by 6.34% across eight retrieval domains.

### What quantization formats does Voyage 3.5 Lite support?

32-bit float, 8-bit integer, and binary precision. Binary rescoring yields up to 6.89% quality improvement, making aggressive compression practical at scale.

### How much cheaper is Voyage 3.5 Lite than competitors?

Voyage 3.5 Lite achieves retrieval quality within 0.3% of Cohere Embed v4 at one-sixth the cost. It also outperforms OpenAI text-embedding-3-large while maintaining a lower per-token price.

### How do I route Voyage 3.5 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 across embedding providers.

### Can I migrate from voyage-3-lite to Voyage 3.5 Lite?

Yes. Voyage 3.5 Lite outperforms voyage-3-lite by 4.28% on average. Migration requires re-embedding your corpus since the model weights are different.

## Links

- [Model page](https://vercel.com/ai-gateway/models/voyage-3.5-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)


If you encounter incorrect information, can’t find what you need, need a capability Vercel doesn’t currently offer, or have any other feedback to report, please do it by following the instructions at https://vercel.com/feedback.md.