This is a fine-tune vs. a newer base model comparison. [Llama 3.3 70B Instruct](/models/meta--llama-3.3-70b-instruct) uses Meta's updated 3.3 training run — better instruction following, improved math reasoning (~77% on MATH benchmark vs ~74% for 3.1), and stronger multilingual coverage — all at roughly the same $0.50–0.90/1M token price range as [Hermes 3 Llama 3.1 70B](/models/nous--hermes-3-llama-3.1-70b). Both carry a 128K context window. Llama 3.3 70B Instruct is the better general-purpose pick for new deployments in 2026. The 3.3 base training improvements are meaningful: coding benchmarks show ~3–5% gains on HumanEval, and instruction adherence on complex multi-constraint prompts improved measurably over 3.1. For teams evaluating 70B inference today, starting with the newer base model rather than a fine-tune on an older base is the lower-risk path. Hermes 3 Llama 3.1 70B retains a niche in structured-reasoning-trace applications. Nous Research's explicit chain-of-thought scaffolding and persona consistency tuning aren't replicated by Meta's vanilla 3.3 instruct fine-tune. If your application depends on extracting structured intermediate reasoning steps — legal analysis pipelines, stepwise audit workflows, or AI assistant products requiring persona stability over 20+ turns — Hermes 3's specific RLHF targets still deliver value that Llama 3.3 vanilla doesn't match out of the box. **Pick Llama 3.3 70B Instruct** for new general-purpose deployments, math-heavy workloads, multilingual tasks, or when you want the most current Meta base model without a fine-tune premium. **Pick Hermes 3 Llama 3.1 70B** if your application specifically requires structured reasoning traces or long-horizon persona consistency that Hermes's RLHF targets explicitly address.