Qwen-MT: Where Speed Meets Smart Translation

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.
• 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.

Translation Quality

Automatic Evaluation

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.

Human Evaluation

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.

Here are some translation examples：

Language Support

How to use

You can easily use Qwen-MT through Qwen API. Here we take a simple scenario of translating from Chinese to English as an example.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

messages = [
    {
        "role": "user",
        "content": "我看到这个视频后没有笑"
    }
]

translation_options = {
    "source_lang": "auto",
    "target_lang": "English"
}

completion = client.chat.completions.create(
    model="qwen-mt-turbo",
    messages=messages,
    extra_body={
        "translation_options": translation_options
    }
)

print(completion.choices[0].message.content)

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.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

messages = [
    {
        "role": "user",
        "content": "而这套生物传感器运用了石墨烯这种新型材料，它的目标物是化学元素，敏锐的“嗅觉”让它能更深度、准确地体现身体健康状况。"
    }
]

translation_options = {
    "source_lang": "Chinese",
    "target_lang": "English",
    "terms": [
        {
            "source": "生物传感器",
            "target": "biological sensor"
        },
        {
            "source": "石墨烯",
            "target": "graphene"
        },
        {
            "source": "化学元素",
            "target": "chemical elements"
        },
        {
            "source": "身体健康状况",
            "target": "health status of the body"
        }
    ]
}

completion = client.chat.completions.create(
    model="qwen-mt-turbo",
    messages=messages,
    extra_body={
        "translation_options": translation_options
    }
)
    
print(completion.choices[0].message.content)

# Response:
# This biological sensor uses graphene, a new material, and its target is chemical elements. Its sensitive "nose" can more deeply and accurately reflect the health status of the body. 

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.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

messages = [
    {
        "role": "user",
        "content": "第二个SELECT语句返回一个数字，表示在没有LIMIT子句的情况下，第一个SELECT语句返回了多少行。"
    }
]

translation_options = {
    "source_lang": "Chinese",
    "target_lang": "English",
    "domains": "The sentence is from Ali Cloud IT domain. It mainly involves computer-related software development and usage methods, including many terms related to computer software and hardware. Pay attention to professional troubleshooting terminologies and sentence patterns when translating. Translate into this IT domain style."
}

completion = client.chat.completions.create(
    model="qwen-mt-turbo",
    messages=messages,
    extra_body={
        "translation_options": translation_options
    }
)
    
print(completion.choices[0].message.content)

# Response:
# The second SELECT statement returns a number that indicates how many rows were returned by the first SELECT statement without LIMIT clause.

For more advanced features, please refer to Qwen API.

Conclusion

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.