Nous Research Hermes 3 Llama 3.1 70B, Meta Llama 3.3 70B Instruct, and Alibaba Qwen 2.5 72B Instruct cover three distinct approaches to the 70B production tier: a community fine-tune focused on agent and reasoning tasks, Meta's own December 2024 alignment update, and Alibaba's previous-generation multilingual flagship. All carry 131K context windows and permissive licenses — Llama 3 community license for both Hermes 3 and Llama 3.3, Qwen license for the 2.5 72B. Hermes 3 Llama 3.1 70B is Nous Research's fine-tune of the Llama 3.1 70B base. The training recipe explicitly targets persona fidelity, XML-tagged reasoning traces, and reduced RLHF softening of system prompt adherence. For agent frameworks where tool-schema compliance, multi-turn role consistency, and explicit reasoning chains matter, Hermes 3 often outperforms the vanilla Meta instruct releases. It is a near-zero-cost swap from Llama 3.1 70B for teams already running that model. Llama 3.3 70B Instruct is Meta's own response to improving 70B alignment. The December 2024 release closes some of the gap that fine-tuners like Nous were addressing, delivering better instruction-following and structured output reliability compared to the 3.1 70B baseline. For general-purpose inference where agent-specific tuning is not the primary concern, Llama 3.3 70B is a simpler choice with broader provider support and a larger fine-tune community. Qwen 2.5 72B is the multilingual standout of the three. It outperforms both Llama variants specifically on CJK and multilingual evaluations, and its 131K context is well-suited for document-length retrieval workloads. It is the standard recommendation for products serving East Asian or Arabic users at this parameter tier. Pick Hermes 3 70B for agent pipelines requiring reasoning traces and tight schema adherence. Pick Llama 3.3 70B for general-purpose production inference. Pick Qwen 2.5 72B for multilingual workloads or when East Asian language quality is a primary criterion.