Voyage AI voyage-3
Voyage's general-purpose embedding model. 1024 dims, 32k context, strong retrieval performance.
Voyage AI voyage-3 is embeddings AI model from Custom, priced at €0.060 per 1M input tokens with a 32K tokens context window.
Pricing
API Integration
Use our OpenAI-compatible API to integrate Voyage AI voyage-3 into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
const vectors = await rw.run("voyage-3", "Hello world", { type: "embed" });
console.log(vectors[0].length); // embedding dimensions
// Or use the embed() method for full control
const res = await rw.embed("voyage-3", ["Hello", "World"]);
for (const item of res.data) {
console.log(item.embedding.length);
}Deep dive — Voyage AI's Voyage AI voyage-3
Voyage AI was founded in 2023 by Tengyu Ma, an associate professor of Computer Science at Stanford and a recognised researcher on optimisation and representation learning, together with co-founders Dawn Song and Christopher Re among the technical advisory team. The company set out to build best-in-class retrieval and reranking models for RAG, with explicit emphasis on domain-specific variants (code, finance, law, multilingual). Voyage AI raised $20M in seed and Series A funding from CRV, Wing VC, Conviction, AME Cloud, Snowflake and others before being acquired by MongoDB for $220M in February 2025 as the foundational embedding layer for Atlas Vector Search. voyage-3 launched in September 2024 as the company's general-purpose flagship and consistently ranks at or near the top of the MTEB and BEIR retrieval leaderboards.
Visit Voyage AI →Voyage AI voyage-3 is a hosted general-purpose embedding model with a 32,000-token context window, the longest commercial embedding context as of late 2024. The default output is a 1,024-dim vector, but the API also supports 256-, 512- and 2,048-dim heads via Matryoshka-style training, all sharing the same backbone. Training used a contrastive retrieval objective over a large curated multilingual mix including code, scientific and financial documents; the team has not disclosed exact token counts. Voyage exposes an input_type parameter ('query' or 'document') for asymmetric search and a separate output_dtype parameter for int8 / binary quantisation, reducing storage cost by up to 32x with a small quality drop. voyage-3 is bundled with a paired reranker (rerank-2) for two-stage retrieval. The model is offered exclusively via hosted API and is also natively integrated into MongoDB Atlas Vector Search since the February 2025 acquisition.
- Parameters
- Undisclosed
- Context
- 32K tokens
- 32,000-token context window (longest commercial embedding context)
- Matryoshka-style heads: 256 / 512 / 1024 / 2048 dimensions
- input_type parameter for asymmetric query / document retrieval
- int8 and binary quantisation for cheap vector storage
- Multilingual coverage with strong English and code performance
- Top-tier MTEB and BEIR retrieval scores
- Native MongoDB Atlas Vector Search integration
- Best for: production RAG, long-document retrieval, MongoDB-backed apps
Not disclosed. Voyage describes a 'large curated multilingual mix including code, scientific and financial documents' plus contrastive negatives.
License: Proprietary commercial API. Available standalone and bundled with MongoDB Atlas Vector Search.
Known limitations
- Closed weights, hosted only
- Cannot fine-tune externally
- Higher latency than OpenAI text-embedding-3 on small inputs
- Multilingual coverage lighter than Cohere v3 for low-resource languages
- Pricing higher than OpenAI for high-volume embedding
Frequently asked questions
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