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The Complete Guide to DeepSeek Models: From V3 to R1 and Beyond

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DeepSeek has emerged as a major player in AI, drawing attention not just for its massive 671B models, V3 and R1, but also for its suite of distilled versions. As interest in these models grows, so does the confusion about their differences, capabilities, and ideal use cases. These questions echo across developer forums, Discord channels, and GitHub discussions. And honestly, the confusion makes sense. DeepSeek’s lineup has expanded rapidly, and without a clear roadmap, it’s easy to get lost in technical jargon and benchmark scores. In this post, we’ll break down the key differences and help you choose the right model for your needs. Let’s rewind to December 2024 when DeepSeek dropped V3. It's a Mixture-of-Experts (MoE) model with 671 billion parameters and 37 billion activated for each token. If you’re wondering what Mixture-of-Experts means, it’s actually a cool concept. Essentially, it means the model can activate different parts of itself depending on the task at hand. Instead of us

The Complete Guide to DeepSeek Models: V3, R1, V4 and Beyond Models Models The Complete Guide to DeepSeek Models: V3, R1, V4 and Beyond Understand the differences among DeepSeek-V3, R1, V3.1, V3.2, V4, and distilled models. Learn how to choose the right model and deploy them securely. Authors Sherlock Xu Last Updated April 24, 2026 Share DeepSeek has emerged as a major player in AI, drawing attention not just for its massive 671B models like V3.1 and R1, but also for its suite of distilled versions. As interest in these models grows, so does the confusion about their differences, capabilities,

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