In this video, I break down the DeepSeek model lineup in a system-design way—what DeepSeek-V3 is, why DeepSeek R1 exists, and what changes when you go to DeepSeek R1-Zero. I connect the dots using the research-paper framing and practical implementation thinking: which model is optimized for general capability, which one is tuned for reasoning-heavy workloads, and what you should expect when a model is trained with reinforcement learning as the core driver. Then I shift from “paper understanding” to “shipping something.” I show you how to run DeepSeek R1 locally with Ollama and how to integrate it from Python so you can actually test reasoning, tool-use patterns, and prompt behavior on your own machine. The big takeaway is choosing the right model for the job: V3-style general performance vs R1-style logical problem solving vs R1-Zero’s RL-first behavior—and validating those differences with quick experiments instead of vibes. If you’re building RAG, agents, or any reasoning-first pipeline, this comparison gives you a clean mental model for picking the right DeepSeek variant and deploying it fast.