Large language models (LLMs) have demonstrated significant performance improvements in numerous tasks, including machine translation (MT). Large reasoning models (LRMs) have further improved existing LLMs with a long reasoning process known as chain of thought (CoT). LRMs excel in reasoning tasks with fixed answers, such as mathematics or coding challenges. Despite success, LRMs often require additional response latency and sometimes meaningless computation overhead, which is known as the “overthinking phenomenon”. This also impedes applying LRMs to practical machine translation, which are more stringent in response time. Therefore, effectively reducing CoT length in LRMs while preserving or improving translation quality has become a critical research problem for MT. In this paper, we present MT-CoT-Compressor, a pipeline to reduce CoT length for machine translation tasks. It comprises of three stages, i.e. CoT summarization, format-quality mixed-reward modeling, and CoT calibration. Experimental results show that our methods effectively reduced the CoT length by 49% to 91% without sacrificing translation quality.