DRT-o1-7B is dedicated to successfully applying long-form reasoning in neural machine translation (MT). The model identifies English sentences suitable for long-form reasoning translation and proposes a multi-agent framework involving three roles: the translator, advisor, and evaluator, to synthesize MT samples. DRT-o1-7B and DRT-o1-14B are trained using Qwen2.5-7B-Instruct and Qwen2.5-14B-Instruct as backbone networks. The main advantage of this model lies in its capability to handle complex linguistic structures and deep semantic understanding, which is crucial for enhancing the accuracy and naturalness of machine translation.