Meta's Llama 3.1 70B, 3.1 8B, and 3.2 3B form a three-tier cost-quality ladder across the same open-weights family, all sharing the Llama 3 community license and a 131K context window. Each was released in mid-to-late 2024, with the 3B and 8B separated by roughly the same quality-per-dollar jump as the 8B to 70B. The 3B Instruct is the floor of this comparison: workable for classification, long-document triage, and content moderation routing where volume and cost dominate quality requirements. Its 131K context is genuinely useful for routing-layer classification over long inputs, which is the one scenario where it holds its own against the 8B. The 8B model covers most practical single-turn and short-context tasks: summarization, translation, lightweight function calling, and structured extraction. MMLU is in the low-to-mid 70s. It does not hold up well under complex multi-step reasoning or long-form generation. At sub-$0.20 per million tokens on most providers it is a reasonable default for general-purpose production use. The 70B is where the quality gap becomes concrete. MMLU around 79-80, better instruction adherence on multi-turn tasks, and a meaningful improvement in reasoning and coding relative to the 8B. The July 2024 release was notable for shipping a 131K context at the 70B tier, which was not standard at the time. Note that Llama 3.3 70B, released December 2024, improves on the 3.1 70B with better instruction-following at the same footprint, so new deployments should default to 3.3 unless pinning a specific checkpoint. Pick the 3B for batch pipelines where cost dominates. Pick the 8B for most general-purpose inference. Pick the 70B when output quality or instruction-following accuracy visibly matters to your users.