Multilingual large language models (LLMs) can answer questions in many languages, but how they internally reason across languages remains poorly understood. In this work, we study multilingual reasoning through a decision-making perspective to investigate how multilingual reasoning unfolds in multilingual LLMs using aligned multiple-choice questions from the mMMLU benchmark. By formulating a controlled setup, presenting the same question in different languages, and tracking the model’s decision from the first token to the final answer choice, we can directly compare how reasoning trajectories evolve across languages. We first demonstrate that, at the representation level, different languages share highly similar activation spaces; however, subtle divergences emerge as decisions propagate through the transformer layers. We then model answer selection as a stepwise trajectory, revealing where language-specific signals arise. These patterns are further confirmed by quantifying deviations along these trajectories, highlighting layers where multilingual processing deviates or converges. Our work provides a controlled, layer-resolved view of multilingual reasoning, shedding light on how LLMs balance shared conceptual understanding with language-specific decision-making.