How much does Voyage AI voyage-code-3 cost via Railwail?

Input: €0.180 per 1M tokens. No monthly minimum, no subscription. Start with €5 free credits.

What is the context window of Voyage AI voyage-code-3?

Voyage AI voyage-code-3 supports a 32K tokens context window — enough for long books, technical manuals, and extended analysis.

How fast is Voyage AI voyage-code-3?

Latency depends on prompt length and load — typically 200ms to 2s for short prompts. We measure p50/p95 in real-time on /rankings.

Is Voyage AI voyage-code-3 better than Text Embedding 3 Large?

It depends on your use case. Voyage AI voyage-code-3 (Custom) and Text Embedding 3 Large (OpenAI) are both strong choices in embeddings. Compare them side-by-side at /compare/voyage-code-3-vs-text-embedding-3-large.

Voyage AI voyage-code-3

Name: Voyage AI voyage-code-3
Brand: Custom
SKU: voyage-code-3
Price: 0.00018 EUR
Availability: InStock

Custom

Embeddings

Voyage's code-specialized embedding model. Up to 32k context, Matryoshka 256-2048 dims, int8/binary support.

Embed with Voyage AI voyage-code-3

Vectorize text and preview the first 8 dimensions as a bar chart.

Outputs a high-dimensional vector you can plug into RAG or search.

Vector preview appears here.

TL;DR·Last updated May 16, 2026

Voyage AI voyage-code-3 is embeddings AI model from Custom, priced at €0.180 per 1M input tokens with a 32K tokens context window.

Try Voyage AI voyage-code-3

Input Text

Direct API access coming soon

Pricing

Price per Generation

Per generationFree

API Integration

Use our OpenAI-compatible API to integrate Voyage AI voyage-code-3 into your application.

Install

npm install railwail

JavaScript / TypeScript

import railwail from "railwail";

const rw = railwail("YOUR_API_KEY");

const vectors = await rw.run("voyage-code-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-code-3", ["Hello", "World"]);
for (const item of res.data) {
  console.log(item.embedding.length);
}

Specifications

Context window

32,000 tokens

Developer

Custom

Deep dive — Voyage AI's Voyage AI voyage-code-3

About Voyage AI

Founded 2023 · Palo Alto, California, USA

Voyage AI was founded in 2023 by Stanford CS professor Tengyu Ma and team, focused on best-in-class retrieval and reranking models for RAG, with a particular emphasis on domain-specific variants. The company has shipped specialised embeddings for finance (voyage-finance-2), law (voyage-law-2), multilingual (voyage-multilingual-2) and code (voyage-code-2 then voyage-code-3). voyage-code-3 launched in December 2024 as the successor to voyage-code-2 and quickly became the top-scoring code embedding model on the CoIR and CodeSearchNet benchmarks. In February 2025 Voyage AI was acquired by MongoDB for $220M, with voyage-code-3 now integrated into MongoDB Atlas Vector Search and recommended for code-aware AI agents and IDE-style retrieval workloads.

Visit Voyage AI →

Architecture

Transformer bi-encoder specialised for code and technical text with Matryoshka heads

Voyage AI voyage-code-3 is a hosted embedding model specialised for source code, technical documentation, commit messages, issues, pull-request reviews and code-mixed natural language. It has the same 32,000-token context window as voyage-3 and supports Matryoshka-style heads at 256 / 512 / 1,024 / 2,048 dimensions, plus int8 / binary quantisation. Training used a contrastive retrieval objective on curated pairs covering more than 30 programming languages (Python, JavaScript, TypeScript, Go, Rust, Java, C/C++, C#, SQL, Bash, etc.) together with technical natural-language text, with deliberate emphasis on code-to-text and text-to-code retrieval as well as code-clone detection. Voyage reports voyage-code-3 outperforming OpenAI text-embedding-3-large by 13.8 points on average across CoIR sub-tasks while costing the same. The model is offered through the Voyage API and natively in MongoDB Atlas Vector Search after the February 2025 acquisition.

Parameters: Undisclosed
Context: 32K tokens

What it can do

Top-tier code embedding model on CoIR and CodeSearchNet
30+ programming languages including Python, JavaScript, TypeScript, Go, Rust, Java
Text-to-code and code-to-text retrieval (e.g. find function from docstring)
32,000-token context window for full-file embedding
Matryoshka heads at 256 / 512 / 1024 / 2048 dimensions
int8 / binary quantisation for cheap storage
MongoDB Atlas Vector Search integration
Best for: code-aware AI agents, IDE retrieval, repository search, ticket-to-code linking