All three models come from DeepSeek and share the same 671-billion-parameter MoE architecture, routing each forward pass through roughly 37B active parameters. What separates them is training objective and release timeline rather than raw scale. DeepSeek V3, released December 2024, was the baseline: a strong general-purpose model that drew attention for matching frontier proprietary models on code and math benchmarks at a fraction of the inference cost. It still works and is hosted on DeepInfra, Fireworks, and OpenRouter, but it is now the legacy variant within this family. DeepSeek R1, also released in early 2025, takes a different approach entirely. Rather than optimizing for throughput, R1 adds explicit chain-of-thought reasoning traces trained via reinforcement learning, which meaningfully improves performance on AIME, MATH, and multi-step logic tasks. The tradeoff is token count: R1 emits substantially more tokens per answer, which drives up latency and cost per query. Its MIT license removes any commercial friction. DeepSeek V3.2, released May 2025, is the cost-efficiency successor to V3. It dropped inference pricing roughly 30 percent relative to V3 while maintaining comparable general-capability benchmarks. For teams that do not need chain-of-thought reasoning traces, V3.2 is simply the better V3 — no architectural reason to stay on the earlier release. Pick R1 if your workload rewards explicit multi-step reasoning and you can absorb the higher per-query token cost. Pick V3.2 for general chat, code generation, and instruction-following at the lowest cost within this family. V3 is worth running only if you already have it pinned and need reproducibility against a specific checkpoint.