Two generations of the same MoE architecture against Alibaba's current-generation 72B, making this comparison more about versioning decisions than fundamental capability gaps. DeepSeek V3 is the December 2024 release — a 671B mixture-of-experts model routing tokens through 8 of 256 experts for roughly 37B active parameters per pass. At launch it was one of the most capable open models on coding, math, and general reasoning benchmarks relative to its serving cost. The 131K context window and broad third-party hosting made it widely adopted. In 2026, V3 is now the legacy variant: the same providers that hosted V3 have largely migrated available capacity toward V3.2, and choosing V3 requires intentional version pinning. DeepSeek V3.2, released May 2025, reduces inference pricing roughly 30% compared to V3 while maintaining comparable or improved benchmark scores. If you are choosing between the two DeepSeek variants without a version-pinning requirement, V3.2 is the correct answer. The commercial license terms for both require the same verification. Both expose a 131K context window. Qwen 3 72B Instruct from April 2025 is a dense 72B model — not MoE — which means different operational characteristics than either DeepSeek variant. Active parameters per forward pass are the full 72B, making per-token cost roughly comparable to V3.2 on many providers while offering more predictable latency variance. Multilingual coverage, especially CJK and Arabic, is where Qwen 3 72B consistently outperforms the DeepSeek V3 line. Strong MMLU and HumanEval scores, Qwen commercial license. Pick DeepSeek V3.2 when you want the best benchmark-per-dollar on code and math tasks and the MoE architecture is acceptable. Pick DeepSeek V3 only if version-pinning requirements force it. Pick Qwen 3 72B for multilingual workloads, more predictable latency, and broader mainstream provider coverage.