# Voyage 3.5

Voyage 3.5 is Voyage AI by MongoDB's highest-accuracy general-purpose embedding model in the Voyage 3.5 lineup. It surpasses OpenAI text-embedding-3-large by 8.26% and Cohere Embed v4 by 1.63% across eight retrieval domains with Matryoshka dimensionality and quantization-aware training.

- **Model ID:** `voyage/voyage-3.5`
- **Type:** embedding
- **Providers:** voyage
- **Context window:** Not applicable
- **Maximum output tokens:** 0
- **Pricing:** $0.06/1M input tokens
- **Canonical page:** https://vercel.com/ai-gateway/models/voyage-3.5

## 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',
  value: 'Sunny day at the beach',
})
```

## About

Voyage 3.5 is Voyage AI by MongoDB's Voyage 3.5 general-purpose 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 surpasses OpenAI text-embedding-3-large by 8.26% and Cohere Embed v4 by 1.63% on average across eight retrieval domains including technical documentation, code, law, and finance.

Like its predecessor, Voyage 3.5 uses Matryoshka learning and quantization-aware training. It supports 32-bit float, 8-bit integer, and binary precision formats. `int8` quantization at 2048 dimensions reduces vector database costs by 83% compared to OpenAI's offering while achieving better retrieval results. Binary rescoring yields up to 6.38% quality improvements, making aggressive compression practical for high-volume production indices.

Voyage 3.5 is the premium tier in Voyage AI by MongoDB's Voyage 3.5 general-purpose lineup. It delivers the highest retrieval accuracy among Voyage 3.5 general-purpose options on evaluated domains. For cost-sensitive workloads where a small accuracy tradeoff is acceptable, voyage-3.5-lite provides comparable coverage at one-third the price.

## What to consider

Voyage 3.5 is Voyage AI by MongoDB's highest-accuracy embedding model in the Voyage 3.5 lineup. If your workload is cost-sensitive and can tolerate a small accuracy gap, voyage-3.5-lite achieves retrieval quality within 0.3% of Cohere Embed v4 at one-sixth the cost.

Use `int8` precision at 2048 dimensions to cut vector storage costs by 83% compared to OpenAI's float embeddings. Binary precision with rescoring adds another compression layer for very large indices.

Voyage 3.5 improves on voyage-3-large across all domains. Switching requires re-embedding your corpus but delivers measurable accuracy gains.

## When to use

### Best For

- **Highest-accuracy semantic search** where retrieval quality directly impacts application outcomes like customer-facing search or legal discovery
- **RAG pipelines** requiring precise document retrieval across technical, legal, and financial content
- **Multi-domain retrieval** where a single embedding model needs to handle diverse content types without domain-specific tuning
- **Enterprise knowledge bases** with long documents that benefit from the context window of 0 tokens
- **Production systems** where int8 and binary quantization reduce infrastructure costs at scale without meaningful quality loss

### Consider Alternatives When

- **Budget is the primary constraint** and you need embedding quality close to Voyage 3.5; voyage-3.5-lite achieves within 0.3% of Cohere Embed v4 at one-sixth the cost
- **Your corpus is exclusively source code** and maximum code retrieval accuracy matters most; voyage-code-3 is purpose-built for code
- **You need multimodal embeddings** that handle images alongside text; Cohere Embed v4 supports interleaved text and image inputs
- **Your workload is latency-critical** at very high query volumes; a lighter model may reduce per-query response time

## Best for

- **Highest-accuracy semantic search:** Retrieval quality directly impacts application outcomes like customer-facing search or legal discovery
- **RAG pipelines:** Precise document retrieval across technical, legal, and financial content
- **Multi-domain retrieval:** A single embedding model handles diverse content types without domain-specific tuning
- **Enterprise knowledge bases:** Long documents benefit from the context window of 0 tokens
- **Production systems:** Int8 and binary quantization reduce infrastructure costs at scale without meaningful quality loss

## Consider alternatives

- **Budget is the primary constraint:** voyage-3.5-lite achieves within 0.3% of Cohere Embed v4 at one-sixth the cost
- **Your corpus is exclusively source code:** voyage-code-3 is purpose-built for code
- **You need multimodal embeddings:** Cohere Embed v4 supports interleaved text and image inputs
- **Your workload is latency-critical:** A lighter model may reduce per-query response time at very high query volumes

## Frequently asked questions

### How does Voyage 3.5 compare to voyage-3-large?

Voyage 3.5 improves on voyage-3-large across all eight evaluated retrieval domains. It also surpasses Cohere Embed v4 by 1.63%. Switching requires re-embedding your corpus.

### What is the difference between Voyage 3.5 and voyage-3.5-lite?

Voyage 3.5 is the premium tier with the highest retrieval accuracy in the Voyage 3.5 lineup. `voyage-3.5-lite` lists at one-third the per-token price of `voyage-3.5`, and reaches retrieval quality within 0.3% of Cohere Embed v4 at one-sixth the cost. Choose Voyage 3.5 when accuracy is the main priority; choose lite when cost is.

### What embedding dimensions does Voyage 3.5 support?

Four dimensions: 2048, 1024, 512, and 256. Matryoshka learning ensures lower-dimensional embeddings retain most of the full-dimension retrieval quality.

### How much can I reduce storage costs with quantization?

`int8` quantization at 2048 dimensions cuts vector database costs by 83% compared to OpenAI's float offering while achieving better retrieval results. Binary precision with rescoring compresses further.

### What domains does Voyage 3.5 cover?

Voyage AI by MongoDB evaluates Voyage 3.5 across eight domains: technical documentation, code, law, finance, web content, multilingual text, long documents, and conversations.

### How do I access Voyage 3.5 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 providers.

### Does Voyage 3.5 support multilingual retrieval?

Yes. Voyage 3.5 is evaluated on multilingual retrieval as one of its eight domains and supports cross-lingual similarity search within a single vector index.

## Links

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