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Qwen3-Embedding is a model series from the Qwen family designed specifically for text embedding and ranking tasks. It builds upon the Qwen3 base/dense models and offers several sizes (0.6B, 4B, 8B parameters), for both embedding and reranking, with high multilingual capability, long‐context understanding, and reasoning. It achieves state-of-the-art performance on benchmarks like MTEB (Multilingual Text Embedding Benchmark) and supports instruction-aware embedding (i.e. embedding task instructions along with queries) and flexible embedding/vector dimension definitions. It is meant for tasks such as text retrieval, classification, clustering, bitext mining, and code retrieval.
Features
- Multiple model sizes (0.6B, 4B, 8B) for embedding and reranking variants
- Multilingual support over 100 languages and dialects, including many low-resource ones; also supports programming languages in code retrieval tasks
- Supports long input context lengths (up to 32K tokens in many cases) for better handling of longer texts
- Instruction‐aware: you can augment input with task instructions to improve performance in specific domains or tasks
- Supports custom embedding dimensions via Matryoshka Representation Learning (MRL) in some variants
- High benchmark performance: e.g. the 8B embedding model ranks No.1 in MTEB multilingual leaderboard as of June 5, 2025; reranker models excel in retrieval contexts
Project Samples
License
Apache License V2.0Follow Qwen3 Embedding
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