Qwen3-Embedding

weights

open(three ungated Apache-2.0 checkpoints, 0.6B/4B/8B)

data

closed(multi-stage weakly-supervised and synthetic mixture described in the paper, not released)

code

partial(evaluation and inference examples in QwenLM/Qwen3-Embedding

license

Apache-2.0(OSI)

All three sizes are released under Apache-2.0. The paper describes the training corpus but does not publish it, and the repository includes evaluation and usage code rather than the pipeline used to produce the checkpoints. This makes Qwen3-Embedding an open-weights release rather than a fully open model.

• https://huggingface.co/api/models/Qwen/Qwen3-Embedding-8B recorded 2026-09-11

  gated: false; private: false; cardData license apache-2.0.

• https://huggingface.co/Qwen/Qwen3-Embedding-8B/raw/main/README.md recorded 2026-09-11

  license: apache-2.0 in the front matter; base_model Qwen/Qwen3-8B-Base; the three embedding sizes and three reranker sizes listed as downloadable; no training data or pretraining code named.

• https://raw.githubusercontent.com/QwenLM/Qwen3-Embedding/main/README.md recorded 2026-09-11

  The product repository points at the Hugging Face and ModelScope collections, the blog, the arXiv paper and the hosted API, and offers usage and evaluation material rather than a pretraining pipeline.

Adoption is measured as Hugging Face downloads across the three embedding checkpoints, most of them for the 0.6B model. The vendor's GGUF conversions and the separate Qwen3-VL-Embedding line are excluded.

• https://huggingface.co/api/models/Qwen/Qwen3-Embedding-0.6B recorded 2026-09-11

  downloads: 7806497; gated: false; private: false.

• https://huggingface.co/api/models/Qwen/Qwen3-Embedding-4B recorded 2026-09-11

  downloads: 2489577; gated: false; private: false.

• https://huggingface.co/api/models/Qwen/Qwen3-Embedding-8B recorded 2026-09-11

  downloads: 2253605; gated: false; private: false.

Qwen3-Embedding-8B led the multilingual MTEB leaderboard when it was released and still scores 70.58, while supporting more than 100 languages, a 32k context window and Matryoshka dimensions. Harrier-OSS has since overtaken it with a score of 74.3; otherwise, Qwen3-Embedding remains at the frontier of the category.

• https://huggingface.co/nvidia/llama-embed-nemotron-8b/raw/main/README.md recorded 2026-09-11

  A third-party MMTEB table dated 21 October 2025 places Qwen3-Embedding-8B third by Borda rank with the highest mean task score in the table, 70.58 - corroborating the vendor figure from outside.

• https://huggingface.co/Qwen/Qwen3-Embedding-8B/raw/main/README.md recorded 2026-09-11

  The card states the 8B model ranks No.1 in the MTEB multilingual leaderboard as of June 5, 2025 with a score of 70.58, over 100 languages, 32k context and embedding dimensions up to 4096.