Hunyuan-MT Technical Report
Authors: name: Mao Zheng, name: Zheng Li, name: Bingxin Qu, name: Mingyang Song, name: Yang Du, name: Mingrui Sun, name: Di Wang

In this report, we introduce Hunyuan-MT-7B, our first open-source
multilingual translation model, which supports bidirectional translation across
33 major languages and places a special emphasis on translation between
Mandarin and several ethnic minority languages as well as dialects.
Furthermore, to serve and address diverse translation scenarios and enhance
model performance at test time, we introduce Hunyuan-MT-Chimera-7B, a
translation model inspired by the slow thinking mode. This model integrates
multiple outputs generated by the Hunyuan-MT-7B model under varying parameter
settings, thereby achieving performance superior to that of conventional
slow-thinking models based on Chain-of-Thought (CoT). The development of our
models follows a holistic training process specifically engineered for
multilingual translation, which begins with general and MT-oriented
pre-training to build foundational capabilities, proceeds to Supervised
Fine-Tuning (SFT) for task-specific adaptation, and culminates in advanced
alignment through Reinforcement Learning (RL) and weak-to-strong RL. Through
comprehensive experimentation, we demonstrate that both Hunyuan-MT-7B and
Hunyuan-MT-Chimera-7B significantly outperform all translation-specific models
of comparable parameter size and most of the SOTA large models, particularly on
the task of translation between Mandarin and minority languages as well as
dialects. In the WMT2025 shared task (General Machine Translation), our models
demonstrate state-of-the-art performance, ranking first in 30 out of 31
language pairs. This result highlights the robustness of our models across a
diverse linguistic spectrum, encompassing high-resource languages such as
Chinese, English, and Japanese, as well as low-resource languages including
Czech, Marathi, Estonian, and Icelandic.