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What is voyage-code-3?

Voyage AI has introduced voyage-code-3, a cutting-edge embedding model meticulously crafted to improve code retrieval performance. This groundbreaking model consistently outperforms OpenAI-v3-large and CodeSage-large by impressive margins of 13.80% and 16.81%, respectively, across a wide array of 32 distinct code retrieval datasets. It supports embeddings in several dimensions, including 2048, 1024, 512, and 256, while offering multiple quantization options such as float (32-bit), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8). With an extended context length of 32 K tokens, voyage-code-3 surpasses the limitations imposed by OpenAI's 8K and CodeSage Large's 1K context lengths, granting users enhanced flexibility. This model employs an innovative Matryoshka learning technique, allowing it to create embeddings with a layered structure of varying lengths within a single vector. As a result, users can convert documents into a 2048-dimensional vector and later retrieve shorter dimensional representations (such as 256, 512, or 1024 dimensions) without having to re-execute the embedding model, significantly boosting efficiency in code retrieval tasks. Furthermore, voyage-code-3 stands out as a powerful tool for developers aiming to optimize their coding processes and streamline workflows effectively. This advancement promises to reshape the landscape of code retrieval, making it a vital resource for software development.

What is voyage-4-large?

The Voyage 4 model family from Voyage AI signifies a pioneering stage in the development of text embedding models, engineered to produce exceptional semantic vectors via a unique shared embedding space that allows for the generation of compatible embeddings among the various models within the series, thus empowering developers to effortlessly integrate models for both document and query embedding, which significantly boosts accuracy while also considering latency and cost factors. This lineup includes the voyage-4-large, the premier model that utilizes a mixture-of-experts architecture to reach state-of-the-art retrieval accuracy while achieving nearly 40% lower serving costs than comparable dense models; voyage-4, which effectively balances quality with performance; voyage-4-lite, which provides high-quality embeddings with a minimized parameter count and lower computational requirements; and the open-weight voyage-4-nano, ideal for local development and prototyping, distributed under an Apache 2.0 license. The seamless interoperability among these four models, all operating within the same shared embedding space, allows for interchangeable embeddings that foster innovative asymmetric retrieval techniques, which can greatly elevate performance across a wide range of applications. This integrated approach equips developers with a dynamic toolkit that can be customized to address various project demands, establishing the Voyage 4 family as an attractive option in the continuously evolving field of AI-driven technologies. Furthermore, the diverse capabilities and flexibility of these models enable organizations to experiment and adapt their embedding strategies to optimize specific use cases effectively.

What is voyage-3-large?

Voyage AI has launched voyage-3-large, a groundbreaking multilingual embedding model that demonstrates superior performance across eight diverse domains, including law, finance, and programming, boasting an average enhancement of 9.74% compared to OpenAI-v3-large and 20.71% over Cohere-v3-English. The model utilizes cutting-edge Matryoshka learning alongside quantization-aware training, enabling it to deliver embeddings in dimensions of 2048, 1024, 512, and 256, while supporting various quantization formats such as 32-bit floating point, signed and unsigned 8-bit integer, and binary precision, which greatly reduces costs for vector databases without compromising retrieval quality. Its ability to manage a 32K-token context length is particularly noteworthy, as it significantly surpasses OpenAI's 8K limit and Cohere's mere 512 tokens. Extensive tests across 100 datasets from multiple fields underscore its remarkable capabilities, with the model's flexible precision and dimensionality options leading to substantial storage savings while maintaining high-quality output. This significant development establishes voyage-3-large as a strong contender in the embedding model arena, setting new standards for both adaptability and efficiency in data processing. Overall, its innovative features not only enhance performance in various applications but also promise to transform the landscape of multilingual embedding technologies.

Media

Media

Media

Integrations Supported

Voyage AI
Cohere
Cohere Embed
Elasticsearch
Gemini
Hugging Face
LangChain
Milvus
MongoDB Atlas
OneSignal
OpenAI
PyTorch
Qdrant
Snowflake
Vespa
Weaviate

Integrations Supported

Voyage AI
Cohere
Cohere Embed
Elasticsearch
Gemini
Hugging Face
LangChain
Milvus
MongoDB Atlas
OneSignal
OpenAI
PyTorch
Qdrant
Snowflake
Vespa
Weaviate

Integrations Supported

Voyage AI
Cohere
Cohere Embed
Elasticsearch
Gemini
Hugging Face
LangChain
Milvus
MongoDB Atlas
OneSignal
OpenAI
PyTorch
Qdrant
Snowflake
Vespa
Weaviate

API Availability

Has API

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

MongoDB

Date Founded

2007

Company Location

United States

Company Website

blog.voyageai.com/2024/12/04/voyage-code-3/

Company Facts

Organization Name

Voyage AI

Date Founded

2023

Company Location

United States

Company Website

blog.voyageai.com/2026/01/15/voyage-4/

Company Facts

Organization Name

MongoDB

Date Founded

2007

Company Location

United States

Company Website

blog.voyageai.com/2025/01/07/voyage-3-large/

Categories and Features

Categories and Features

Categories and Features

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