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Voyage 4

Voyage 4

By Voyage AI
Model family: Voyage
voyage-4 is a mid-tier text embedding model in the Voyage 4 family, designed for general-purpose and multilingual retrieval. It supports 32,000-token context and configurable output dimensions (256, 512, 1024 default, 2048). Multiple quantization formats are available: float32, int8, uint8, binary, and ubinary, enabling trade-offs between storage cost and retrieval accuracy.

A defining feature of the Voyage 4 series is a shared embedding space: all four models (voyage-4-large, voyage-4, voyage-4-lite, voyage-4-nano) produce compatible vectors. This enables asymmetric retrieval where documents are embedded once with a larger model and queries are served using a smaller, faster model without re-indexing. voyage-4 approaches the retrieval quality of voyage-3-large while maintaining efficiency of a mid-sized model. Evaluated across the 29-dataset RTEB benchmark, it outperforms OpenAI text-embedding-3-large by a wide margin. Available via the Voyage API and through MongoDB Atlas.
Text
Released: January 15, 2026

Overview

Text embedding model optimized for general-purpose and multilingual retrieval quality. Supports a 32,000-token context window and configurable output dimensions (256, 512, 1024, 2048) via Matryoshka Representation Learning. Part of the Voyage 4 series, which shares a common embedding space enabling asymmetric retrieval across models in the family.

🔗Embeds🔍Information retrieval🔎Semantic search

Pricing

Source

Compare Voyage 4 with other models listed in the same vendor pricing tiers and context lengths.

Tier

Standard

Model Input Cached input Output Unit
rerank 2.5 Voyage AI
$0.05 - $0 per 1M tokens
Rerank 2.5 lite Voyage AI
$0.02 - $0 per 1M tokens
Voyage 4 This model Voyage AI
$0.06 - $0 per 1M tokens
Voyage 4 large Voyage AI
$0.12 - $0 per 1M tokens
Voyage 4 lite Voyage AI
$0.02 - $0 per 1M tokens
Voyage code 2 Voyage AI
$0.12 - $0 per 1M tokens
Voyage code 3 Voyage AI
$0.18 - $0 per 1M tokens
Voyage context 3 Voyage AI
$0.18 - $0 per 1M tokens
Voyage finance 2 Voyage AI
$0.12 - $0 per 1M tokens
Voyage law 2 Voyage AI
$0.12 - $0 per 1M tokens
Voyage multilingual 2 Voyage AI
$0.12 - $0 per 1M tokens
Voyage multimodal 3 Voyage AI
$0.12 - $0 per 1M tokens
Voyage multimodal 3.5 Voyage AI
$0.12 - $0 per 1M tokens

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
Website: voyageai.com
View Company Profile

Other models from this family

  • Voyage
    Voyage 4 large
    By Voyage AI
    voyage-4-large is a state-of-the-art general-purpose and multilingual text embedding model using a mixture-of-experts (MoE) architecture. It delivers frontier retrieval accuracy with serving costs 40% lower than comparable dense models. Features a 32K context window, flexible output dimensions (256, 512, 1024, 2048), and multiple quantization options including float, int8, uint8, binary, and ubinary.
    🔗Embeds🔍Text classification
    Text
    Released 5mo ago
  • Voyage
    Voyage multimodal 3.5
    By Voyage AI
    Multimodal embedding model that vectorizes interleaved text, images, and videos into a shared vector space for retrieval tasks. Uses a single transformer encoder for all modalities, eliminating the modality gap. Supports document screenshots, PDFs, slides, figures, tables, and video frames. Context window of 32,000 tokens with Matryoshka embeddings at 256, 512, 1024, and 2048 dimensions.
    🔗Embeds🔍Information retrieval🔎Search
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    Released 5mo ago
  • Voyage
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    By Voyage AI
    Lightweight, general-purpose text embedding model optimized for low latency and cost. Supports a 32K token context window and flexible output dimensions (2048, 1024, 512, 256) via Matryoshka learning. Offers multiple quantization formats including float, int8, uint8, binary, and ubinary. Part of the Voyage 4 series with a shared embedding space across models.
    🔗Embeds🔍Text classification
    Text
    Released 5mo ago
View all models from this family

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Last updated: July 8, 2026
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