Three large open-weights models with very different architecture and use-case profiles. The most important comparison factor here is not raw benchmark numbers — it is what each model can and cannot do in practice. Llama 3.1 405B Instruct is Meta's 405B dense model from July 2024, the most capable general-purpose entry in this group. The 131K context window enables long-document analysis, contract review, and complex multi-turn tasks at a scale unavailable in the other two models here. MMLU scores ranked near the top of open models at release, and the Llama 3 community license supports commercial use. Multi-GPU serving requirements mean hosting is limited but exists on Lambda Labs, Fireworks, and a handful of others. Mixtral 8x22B Instruct is Mistral AI's April 2024 mixture-of-experts model — 141B total parameters routing through 2 of 8 experts for roughly 39B active parameters per forward pass. This makes its effective inference cost significantly lower than 405B despite producing competitive benchmark scores on reasoning and coding tasks. The 64K context window is shorter than either peer but covers most practical workloads. Apache 2.0 license with broad provider support across Fireworks, Together AI, and Replicate. Nemotron-4 340B Instruct is the outlier: a 340B dense model from NVIDIA tuned specifically for synthetic data generation, with a 4K context ceiling that disqualifies it from most production inference use cases. If you are generating training datasets and need a large dense reference model on NVIDIA NIM, that is the specific scenario it addresses. Pick Llama 3.1 405B when long-context capability and frontier reasoning quality justify the serving cost. Pick Mixtral 8x22B for strong capability at much lower effective inference cost under Apache 2.0. Pick Nemotron-4 340B only for synthetic data generation tasks it was specifically designed for.