What is Qwen-MT？
Qwen-MT is a machine translation model developed by Alibaba Cloud’s Tongyi Qianwen team, built upon the powerful Qwen3 architecture. It supports high-quality translation between 92 languages, covering over 95% of the global population, and is designed to meet diverse cross-lingual communication needs. Leveraging a lightweight Mixture of Experts (MoE) architecture, Qwen-MT delivers low latency and cost-efficiency, with API usage priced as low as $0.5 per million output tokens. The model supports terminology intervention, domain-specific prompting, and translation memory features, allowing users to customize translation styles based on their specific needs. Qwen-MT demonstrates exceptional translation quality and fluency in both automatic and human evaluations, making it an ideal choice for efficient and intelligent translation.
Key Features of Qwen-MT
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Multilingual Support: Enables translation between 92 major languages and dialects, covering over 95% of the global population, and addressing a wide range of cross-language communication needs.
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Highly Customizable: Offers features like terminology intervention, domain prompts, and translation memory. Users can tailor translation styles to suit complex professional scenarios.
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Low Latency and Cost: Built on a lightweight MoE architecture, Qwen-MT ensures fast response times and low API costs (as low as $0.5 per million output tokens), ideal for high-concurrency and real-time applications.
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High-Quality Translation: Excels in both automated and human evaluations, providing accurate and fluent translations suitable for various domains.
Technical Principles Behind Qwen-MT
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Powerful Foundation Model: Based on the Qwen3 architecture and trained on trillions of multilingual and translation-specific data, enhancing multilingual understanding and performance.
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Reinforcement Learning Optimization: Utilizes reinforcement learning techniques to further improve translation accuracy and fluency, optimizing overall model performance.
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Lightweight MoE Architecture: Employs a Mixture of Experts (MoE) structure for efficient computation and fast response, significantly reducing API usage costs.
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Customizable Translation Features: Supports terminology control, domain prompts, and translation memory. Users can input custom parameters and prompts to ensure translations meet specific requirements.
Project Links for Qwen-MT
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Official Website: https://qwenlm.github.io/blog/qwen-mt/
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Online Demo: https://huggingface.co/spaces/Qwen/Qwen3-MT-Demo
Application Scenarios for Qwen-MT
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Cross-Language Content Creation and Publishing: Enables media outlets, social platforms, and content creators to translate content into multiple languages, broadening reach and enhancing engagement.
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Enterprise Globalization: Facilitates multilingual support for multinational companies, customer service, and business communication—accelerating global expansion and improving customer satisfaction.
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Education Sector: Assists in online education, academic research, and language learning by providing multilingual translation, promoting resource sharing and international academic collaboration.
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Legal and Government Services: Supports translation of legal documents and government information into multiple languages, ensuring legal accuracy and enhancing global public service accessibility.
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Technology and Development: Enables software localization, API integration, and technical document translation, helping developers achieve efficient localization and technical communication.
