Three models that force an honest look at how active-parameter economics actually work in deployment. Llama 3.1 70B Instruct is Meta's July 2024 dense 70B model with a 131K context window and the Llama 3 community license. It was a meaningful milestone as the first 70B-class model with that context length, and it still holds up on general benchmarks. For teams that have not migrated to 3.3 70B, the weight is the same — the main reason to stay is checkpoint stability for fine-tuned adapters or cached prompt distributions. Phi-3.5 MoE Instruct from Microsoft is the unusual option here. Released August 2024 with 41.9B total parameters but only approximately 6.6B active parameters per forward pass, it achieves reasoning benchmark scores that compete with dense 14B models at the inference cost of a 7B model. MIT license removes commercial friction entirely. Context window is 131K. The catch is provider coverage: Azure AI is the primary route, and aggregate throughput options are thinner than Llama equivalents. It is a strong fit for teams already on Azure infrastructure. Qwen 3 32B Instruct, Alibaba's April 2025 mid-tier model at 32 billion dense parameters, 131K context, and Qwen commercial licensing, slots above both in parameter count while offering multilingual performance that neither competitor matches. On standard English benchmarks it is competitive with Llama 3.1 70B despite fewer parameters. Pick Llama 3.1 70B if you are maintaining a pinned 70B checkpoint with existing fine-tuned adapters and do not want migration risk. Pick Phi-3.5 MoE for Azure deployments where active-parameter inference cost matters and multilingual breadth is secondary. Pick Qwen 3 32B for new deployments where multilingual quality and competitive English benchmarks at the 32B cost tier are the priority.