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Voyage code 3

voyage-code-3 is an embedding model built for code retrieval workloads including text-to-code, code-to-code, and docstring-to-code retrieval. It supports a 32K-token context window, compared to 8K for OpenAI alternatives. Matryoshka learning enables nested embeddings at 256, 512, 1024, and 2048 dimensions from a single vector, allowing dimension reduction without re-embedding. Quantization options include float32, int8, uint8, binary, and ubinary formats, enabling up to 32x storage reduction. Trained on a diverse corpus covering 300+ programming languages with real-world query-code pairs. Available via Voyage API, AWS SageMaker marketplace, and on-premises deployment. Binary rescoring further recovers retrieval quality when needed.
Coding Gen 2
Released: December 4, 2024

Overview

Text embedding model optimized for code retrieval. Supports Matryoshka embeddings at 256, 512, 1024, and 2048 dimensions with quantized formats (float, int8, uint8, binary, ubinary) for up to 32x storage reduction. Features a 32K-token context window. Outperforms OpenAI text-embedding-3-large by 13.80% on 32 code retrieval datasets covering text-to-code, code-to-code, and docstring-to-code tasks.

About Voyage AI

Voyage AI provides best-in-class embedding models and rerankers for search and retrieval over unstructured data, used to power retrieval-augmented generation (RAG) and AI applications. It offers general-purpose, domain-specific (finance, legal, code) and company-specific fine-tuned models. Founded in 2023 and based in Palo Alto, the company was acquired by MongoDB, Inc. in February 2025 and now operates as a MongoDB subsidiary.

Industry: Artificial Intelligence
Location: Palo Alto, California, US
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Last updated: June 24, 2026
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