Llava-o1 is a vision-language reasoning model that introduces a structured approach to improve performance on tasks requiring detailed, step-by-step reasoning. Unlike traditional VLMs, Llava-o1 divides reasoning into four distinct stages and uses a specialized dataset for training. It demonstrates significant improvements over its base model and larger VLMs in various benchmarks. The model's structured design enhances both accuracy and usability in AI systems, offering interpretability, scalability, and versatility across diverse domains.