The cost gap is stark: [Llama 3.1 405B Instruct](/models/meta--llama-3.1-405b-instruct) runs $3–6/1M tokens at competitive providers; [Llama 3.3 70B Instruct](/models/meta--llama-3.3-70b-instruct) lands at $0.50–0.90/1M tokens — a 5–7x multiplier for roughly 5–8% more capability on most benchmarks. Llama 3.3 70B scores around 77% on MMLU and 77% on MATH with improved 3.3 training; Llama 3.1 405B scores ~88% MMLU and ~73% MATH (the 405B's math tuning is weaker relative to its size). Both support 128K context. Llama 3.3 70B Instruct handles the vast majority of production workloads more efficiently. RAG over enterprise knowledge bases, multi-document summarization, code generation for standard patterns, and agentic pipelines with well-defined tool schemas all run well within 70B capability. At $0.70/1M tokens, you can process 100M tokens for what 405B charges for 11M — the arithmetic matters at scale. Llama 3.1 405B Instruct justifies the premium for tasks where reasoning depth is measurably load-bearing. Complex legal document analysis spanning 50K+ tokens with nuanced cross-reference requirements, novel algorithm design, high-stakes multi-constraint optimization, or serving as the "judge" model in an LLM-eval pipeline where output quality directly impacts downstream accuracy — these are the cases where 405B's parameter advantage translates into output quality you can't compensate for with prompt engineering on a 70B. **Pick Llama 3.3 70B Instruct** for 90%+ of production workloads — it's 5–7x cheaper with comparable output on most tasks. **Pick Llama 3.1 405B Instruct** when complex reasoning depth, nuanced long-document analysis, or LLM-judge accuracy make the premium cost-justified at your volume.