Here we introduce the latest update of Qwen-MT (qwen-mt-turbo) via Qwen API . This update builds upon the powerful Qwen3, leveraging trillions multilingual and translation tokens to comprehensively enhance the model’s multilingual understanding and translation capabilities. By integrating reinforcement learning techniques, the model achieves significant improvements in translation accuracy and linguistic fluency.
Multilingual Support for 92 Languages : Qwen-MT enables high-quality translation across 92 major official languages and prominent dialects, covering over 95% of the global population to meet diverse cross-lingual communication needs.
High Customizability : The new version provides advanced translation capabilities such as terminology intervention, domain prompts and translation memory. By enabling customizable prompt engineering, it delivers optimized translation performance tailored to complex, domain-specific, and mission-critical application scenarios.
Low Latency & Cost Efficiency : By leveraging a lightweight Mixture of Experts (MoE) architecture, Qwen-MT achieves high translation performance with faster response times and significantly reduced API costs (as low as $0.5 per million output tokens). This is particularly well-suited for high-concurrency environments and latency-sensitive applications.
We evaluate Qwen-MT on multi-domain translation benchmark, specifically Chinese-English and English-German translation, as well as WMT24 multilingual translation benchmark. Experimental results demonstrate that Qwen-MT significantly outperforms comparably-sized models including GPT-4.1-mini, Gemini-2.5-Flash, and Qwen3-8B. Notably, even when compared to state-of-the-art large language models such as GPT-4.1, Gemini-2.5-Pro, and Qwen3-235B-A22B, Qwen-MT maintains competitive translation quality while its optimized lightweight architecture enables rapid translation processing.
Automatic evaluation methods have inherent limitations, we further implemented human evaluation protocols leveraging real-world translation data across ten major languages: Chinese, English, Japanese, Korean, Thai, Arabic, Italian, Russian, Spanish, and French. Our rigorous evaluation framework involved three independent professional translators scoring each test sample, followed by systematic cross-validation procedures to ensure both objectivity and reliability of the assessment outcomes. Notably, Qwen-MT achieved superior performance metrics, demonstrating significant advantages in both acceptance rates and excellence rates, validating its exceptional translation capabilities in practical deployment scenarios.
You can easily use Qwen-MT through Qwen API. Here we take a simple scenario of translating from Chinese to English as an example.
Qwen-MT supports features such as terminology intervention, domain prompts, and translation memory. For instance, in a translation scenario involves specialized terms, users can predefine key terminology pairs and inject them as parameters into the model, ensuring consistent application of user-specified lexicon throughout the output.
Moreover, translation style must adapt to contextual nuances. For example, in legal and official contexts, formal register is imperative, whereas social media communication demands a conversational tone. To ensure appropriate stylistic adaptation, users can provide contextual details and stylistic preferences in natural language alongside their source text.
For more advanced features, please refer to Qwen API .
Qwen-MT is an advanced machine translation model that supports translations among 92 languages. It aims to provide global users with smart, flexible, and efficient translation experience through APIs. While "faithfulness, fluency, and elegance" remains an ongoing journey filled with challenges, we are persistently exploring and improving. Looking ahead, we will continue to enhance translation accuracy and naturalness, expand coverage to more languages, ultimately dismantling language barriers to realize truly borderless communication.
DEMO API DISCORD Introduction Here we introduce the latest update of Qwen-MT (qwen-mt-turbo) via Qwen API. This update builds upon the powerful Qwen3, leveraging trillions multilingual and translation tokens to comprehensively enhance the model’s multilingual understanding and translation capabilities. By integrating reinforcement learning techniques, the model achieves significant improvements in translation accuracy and linguistic fluency. Key Features: Multilingual Support for 92 Languages: Qwen-MT enables high-quality translation across 92 major official languages and prominent dialects, covering over 95% of the global population to meet diverse cross-lingual communication needs.
通义千问 这条官方动态围绕「Qwen 发布 Qwen-MT 翻译模型更新」展开，英文标题为 “Qwen-MT: Where Speed Meets Smart Translation”。正文重点落在开发者接口、代码任务和调用边界，需要结合官方发布内容理解它对模型使用和开发者接入的影响。
对用户来说，这类信息最有价值的部分是判断新能力是否已经可用、适合哪些任务，以及调用时可能受到哪些版本、地区、权限或产品形态限制。
放到 AI API 中转站评测场景，重点要看服务商是否真实支持相关模型或能力，模型名称、返回行为、延迟、错误信息、上下文限制和价格说明是否能相互印证。
后续自测时可以围绕「Qwen 发布 Qwen-MT 翻译模型更新」设计更具体的探针任务：复杂提示词、连续对话、工具调用、多模态输入或代码任务都能帮助区分真实能力和只写在页面上的模型列表。