Both models are fine-tuned on the same Llama 3.1 70B base, so the hardware cost is identical — the difference is entirely in what the fine-tuning optimized for. DeepSeek R1 Distill Llama 70B transfers chain-of-thought reasoning traces from DeepSeek's larger R1 model into the 70B weights, producing a model that thinks through problems step by step before answering. Hermes 3 from Nous Research targets agentic tool use, structured output, and function-calling reliability. On math and logic benchmarks, R1 Distill punches well above the typical 70B weight class — it approaches performance you'd expect from much larger models on tasks like AIME and MATH thanks to the distilled reasoning patterns. The tradeoff is verbosity: the model generates longer responses with visible reasoning traces, which increases output token cost and latency. For complex reasoning tasks — multi-step math word problems, logical deduction chains, or research synthesis where showing the work matters — [DeepSeek R1 Distill Llama 70B](/models/deepseek--deepseek-r1-distill-llama-70b) is a compelling option. You get near-frontier reasoning quality at 70B inference cost. Hermes 3 is built for agentic pipelines: reliable JSON schema output, consistent function-calling, and structured extraction from unstructured text. If you're building a tool-calling agent that needs deterministic output formatting across thousands of API calls, Hermes 3's alignment work on structured generation is more directly useful than chain-of-thought reasoning. Check provider availability on [Hermes 3 Llama 3.1 70B's model page](/models/nous--hermes-3-llama-3.1-70b). **Pick DeepSeek R1 Distill** for reasoning-heavy tasks where accuracy on hard problems matters. **Pick Hermes 3** for agentic tool use and structured output reliability.