Voyage Embeddings

weights

open(voyage-4-nano ships model.safetensors on the Hub, ungated and public

data

closed(training corpus not released or described for any SKU)

code

closed(no training pipeline

license

Apache-2.0(governs voyage-4-nano, the only SKU whose weights are distributed

Of the four Voyage 4 models, only voyage-4-nano ships Apache-2.0 weights on the Hub; voyage-4-large, voyage-4 and voyage-4-lite are API-only, with Hub repositories that hold a tokenizer and config but no weight file. Training data and training code remain undisclosed for every tier, including voyage-4-nano's.

• https://blog.voyageai.com/2026/01/15/voyage-4/ recorded 2026-09-11

  'voyage-4-nano. Our first open-weight model, freely available on Hugging Face under the Apache 2.0 license.' The other three are announced as API models; no corpus or training pipeline is described for any of them.

• https://huggingface.co/api/models/voyageai/voyage-4-large recorded 2026-09-11

  voyageai/voyage-4-large: gated false, but the file list is .gitattributes, README.md, config.json, merges.txt, tokenizer.json, tokenizer_config.json and vocab.json — tokenizer and config only, no weight shards. The flagship is API-only.

• https://huggingface.co/api/models/voyageai/voyage-4-nano recorded 2026-09-11

  voyageai/voyage-4-nano: gated false, private false, license:apache-2.0, pipeline_tag feature-extraction, and model.safetensors plus LICENSE.txt in the file list. Real downloadable weights.

Adoption is recorded as reported traction rather than a download count because the only Hugging Face figure available is for voyage-4-nano alone, and the flagship tiers are API-only. The stronger signal is native distribution inside MongoDB Atlas, which acquired Voyage AI and now serves these models through its own Embedding and Reranking API.

• https://huggingface.co/api/models/voyageai/voyage-4-nano recorded 2026-09-11

  voyageai/voyage-4-nano records 302,582 downloads over the trailing 30 days and 143 likes.

• https://www.mongodb.com/company/blog/product-release-announcements/introducing-the-embedding-and-reranking-api-on-mongodb-atlas recorded 2026-09-11

  'Voyage AI's models are now natively available on MongoDB Atlas via the new Embedding and Reranking API'; the platform is 'already trusted by tens of thousands of customers, including over 75% of Fortune 100 companies'. The customer figure is Atlas's, not the models'.

Voyage-4-large leads RTEB, the benchmark it is measured on, but RTEB is Voyage's own benchmark, run against publisher-chosen baselines, rather than an independent public leaderboard. That keeps it a step below Harrier-OSS, which leads MMTEB v2 at 74.3; the benchmark reflects voyage-4-large, the flagship tier, not the voyage-4-nano checkpoint whose license and weights are described above.

• https://blog.voyageai.com/2026/01/15/voyage-4/ recorded 2026-09-11

  'Overall, voyage-4-large is the top-performing model, surpassing voyage-4, voyage-4-lite, Gemini Embedding 001, Cohere Embed v4, and OpenAI v3 Large by an average of 1.87%, 4.80%, 3.87%, 8.20%, and 14.05%, respectively' over 'all 29 datasets in the comprehensive Retrieval Embedding Benchmark (RTEB)'; 'voyage-4-large is the first production-grade embedding model that utilizes a mixture-of-experts architecture ... serving costs 40% lower than comparable dense models'.

• https://docs.voyageai.com/docs/embeddings recorded 2026-09-11

  The Voyage embeddings docs list voyage-4-large, voyage-4 and voyage-4-lite as the current text embedding models, with the voyage-3 and voyage-2 families marked as earlier.