Voyage 3 large
Text embedding model optimized for general-purpose and multilingual retrieval across law, finance, code, and long-document domains. Supports a 32K-token context window, flexible output dimensions (256, 512, 1024, 2048), and multiple quantization formats (float32, int8, uint8, binary) via Matryoshka and quantization-aware training. Accessed via API.
- Type
- Text
- Weights
- Closed
- Likes
- 21
Model hub
voyageai/voyage-3-large
- Repo
- voyageai/voyage-3-large
- Access
- Public
More from Voyage AI
10
-
Rerank 2 lite
Text
rerank-2-lite is a lightweight multilingual cross-encoder reranker from Voyage AI. It scores and reorders can…
-
rerank 2.5
Text
A reranking model that reorders search results to improve retrieval accuracy, with instruction-following capa…
-
Rerank 2.5 lite
Text
A latency-optimized reranker that refines retrieval results with instruction-following capabilities. Supports…
-
Voyage
Text
voyage is a text embedding model that converts text into dense vector representations for semantic retrieval.…
-
Voyage 01
Text
voyage-01 is a text embedding model by Voyage AI, designed for retrieval-augmented generation (RAG) and seman…
-
Voyage 3 lite
Text
Lightweight general-purpose text embedding model optimized for latency and cost. Generates 512-dimensional ve…
-
Voyage 4
Text
Text embedding model optimized for general-purpose and multilingual retrieval quality. Supports a 32,000-toke…
-
5.6k DL
Voyage 4 large
Text
voyage-4-large is a state-of-the-art general-purpose and multilingual text embedding model using a mixture-of…
About Voyage AI
1
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.