Three genuinely different philosophies at the open-weights frontier. DeepSeek V3.2 is a 671B-parameter MoE from a Chinese research lab that routes each token through around 37B active parameters — it matches or exceeds frontier proprietary models on code and math benchmarks at a fraction of the inference cost, and its May 2025 pricing drop made it roughly 30 percent cheaper than V3. The context window is 128K. The licensing is DeepSeek's own terms, so commercial teams should confirm their legal review before production. Llama 3.3 70B Instruct is Meta's December 2024 dense model, 70 billion parameters with a 131K context window under a Llama 3 community license. It is the most commercially clean option in this comparison for teams without in-house legal review — permissive, broadly available across dozens of providers, and straightforward to self-host on two to four A100s. Benchmark scores on MMLU and instruction-following improved meaningfully over 3.1 70B despite the same parameter count, which suggests better training data curation. Mistral Large 2 is Mistral AI's 123B-parameter flagship, released in July 2024 with a 128K context window and explicit design focus on European multilingual quality, function calling, and reduced hallucination rate compared to Mistral's earlier 7B and 8x7B releases. It carries a Mistral Research License, which is more restrictive than Apache — commercial deployment requires Mistral API access or a separate enterprise agreement. Pick DeepSeek V3.2 when raw cost-adjusted quality is the top priority and you can handle the licensing review. Pick Llama 3.3 70B when Apache-permissive terms and broad provider coverage matter more than maximum quality. Pick Mistral Large 2 when multilingual European quality and Mistral's managed API ecosystem are the deciding factors.