AlphaMaze is a decoder language model designed specifically for solving visual reasoning tasks. It demonstrates the potential of language models in visual reasoning through training on maze-solving tasks. The model is built upon the 1.5 billion parameter Qwen model and is trained with Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). Its main advantage lies in its ability to transform visual tasks into text format for reasoning, thereby compensating for the lack of spatial understanding in traditional language models. The development background of this model is to improve AI performance in visual tasks, especially in scenarios requiring step-by-step reasoning. Currently, AlphaMaze is a research project, and its commercial pricing and market positioning have not yet been clearly defined.