Highlights

The Qwen3 Embedding model series is specifically designed for text embedding tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.

Exceptional Versatility: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks No.1 in the MTEB multilingual leaderboard (as of June 5, 2025, score 70.58).

Comprehensive Flexibility: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for embedding models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, these models allow for flexible vector definitions across all dimensions, support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.

Multilingual Capability: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities.

Qwen3-Embedding-8B has the following features:

• Model Type: Text Embedding
• Supported Languages: 100+ Languages
• Number of Paramaters: 8B
• Context Length: 32k
• Embedding Dimension: Up to 4096, supports user-defined output dimensions ranging from 32 to 4096

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to the model’s blog, GitHub.