Large Language Models (LLMs) have introduced reasoning capabilities through multi-step problem-solving processes. These models predominantly perform reasoning in English, limiting their effectiveness in other languages. This paper introduces Bode Reasoning, a Portuguese-language reasoning approach built upon fine-tuned Qwen3-4B and Qwen3-4B-Thinking models, and the Bode Reasoning Portuguese Dataset, comprising 13,961 instances from Brazilian examinations and translated datasets. Through supervised fine-tuning, the proposed approach successfully shifts the reasoning process to Brazilian Portuguese while reducing output verbosity. Experimental evaluation demonstrates that fine-tuned models generate Portuguese reasoning in 86-98.7% of outputs and achieve superior lexical alignment with reference answers. However, this specialization results in moderate mean G-Eval and accuracy degradation across diverse multiple-choice question types, highlighting inherent trade-offs in adapting multilingual reasoning models.