These three models all occupy the high-end open-weights tier but represent distinct architectural and organizational bets on how to deliver frontier-class capability in a deployable package. DeepSeek V3.2 is a 671-billion-parameter MoE with roughly 37B active parameters per token. Released May 2025 with an ~30 percent reduction in inference cost versus its predecessor V3, it delivers strong general-purpose benchmarks — code, math, instruction-following — at a price point that meaningfully undercuts hosting a 405B dense model. The DeepSeek license applies; verify commercial terms before production deployment. Meta's Llama 3.1 405B Instruct is a 405-billion-parameter dense model, released July 2024 under the Llama 3 community license. Dense means every parameter participates in every forward pass, which drives both inference cost and memory requirements substantially higher than comparable MoE models. The 131K context window is a genuine advantage. Llama 3.1 405B set a benchmark at launch as the best-performing openly licensed dense model in its class, but it is resource-intensive to host and increasingly difficult to justify cost-wise against modern MoE alternatives. Mixtral 8x22B Instruct is Mistral AI's 141B total / 39B active MoE, carrying 64K context and released under the Apache 2.0 license, making it one of the most permissively licensed large-scale open models available. It offers a middle path — capable enough for most production use cases, cheaper to host than Llama 405B, and freely redistributable without commercial restrictions. Pick DeepSeek V3.2 for maximum capability per dollar with flexible hosting options. Pick Mixtral 8x22B if Apache 2.0 licensing is a hard requirement and you need broad provider support. Pick Llama 3.1 405B only if you have specific reasons to anchor on the Meta ecosystem or need the longest context in a dense non-MoE architecture.