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Description
The Voyage 4 model family from Voyage AI represents an advanced era of text embedding models, crafted to yield superior semantic vectors through an innovative shared embedding space that allows various models in the lineup to create compatible embeddings, thereby enabling developers to seamlessly combine models for both document and query embedding, ultimately enhancing accuracy while managing latency and cost considerations. This family features voyage-4-large, the flagship model that employs a mixture-of-experts architecture, achieving cutting-edge retrieval accuracy with approximately 40% reduced serving costs compared to similar dense models; voyage-4, which strikes a balance between quality and efficiency; voyage-4-lite, which delivers high-quality embeddings with fewer parameters and reduced compute expenses; and the open-weight voyage-4-nano, which is particularly suited for local development and prototyping, available under an Apache 2.0 license. The interoperability of these four models, all functioning within the same shared embedding space, facilitates the use of interchangeable embeddings, paving the way for innovative asymmetric retrieval strategies that can significantly enhance performance across various applications. By leveraging this cohesive design, developers gain access to a versatile toolkit that can be tailored to meet diverse project needs, making the Voyage 4 family a compelling choice in the evolving landscape of AI-driven solutions.
Description
Voyage AI has unveiled voyage-code-3, an advanced embedding model specifically designed to enhance code retrieval capabilities. This innovative model achieves superior performance, surpassing OpenAI-v3-large and CodeSage-large by averages of 13.80% and 16.81% across a diverse selection of 32 code retrieval datasets. It accommodates embeddings of various dimensions, including 2048, 1024, 512, and 256, and provides an array of embedding 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 a context length of 32 K tokens, voyage-code-3 exceeds the limitations of OpenAI's 8K and CodeSage Large's 1K context lengths, offering users greater flexibility. Utilizing an innovative approach known as Matryoshka learning, it generates embeddings that feature a layered structure of varying lengths within a single vector. This unique capability enables users to transform documents into a 2048-dimensional vector and subsequently access shorter dimensional representations (such as 256, 512, or 1024 dimensions) without the need to re-run the embedding model, thus enhancing efficiency in code retrieval tasks. Additionally, voyage-code-3 positions itself as a robust solution for developers seeking to improve their coding workflow.
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Gemini
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Hugging Face
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Milvus
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MongoDB Atlas
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OpenAI
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Vespa
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Voyage AI
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Integrations
Cohere Embed
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Elasticsearch
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Gemini
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Hugging Face
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Milvus
Yes
MongoDB Atlas
No
OpenAI
No
Qdrant
Yes
Vespa
Yes
Voyage AI
No
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Free Trial
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Free Version
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Deployment
Web-Based
Yes
On-Premises
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iPhone App
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iPad App
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Android App
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Windows
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Mac
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Linux
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Chromebook
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Types of Training
Training Docs
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Webinars
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Live Training (Online)
Yes
In Person
No
Vendor Details
Company Name
Voyage AI
Founded
2023
Country
United States
Website
blog.voyageai.com/2026/01/15/voyage-4/
Vendor Details
Company Name
MongoDB
Founded
2007
Country
United States
Website
blog.voyageai.com/2024/12/04/voyage-code-3/