The Llama 3.1 8B Instruct, Llama 3.2 1B Instruct, and Llama 3.2 3B Instruct are all Meta open-weights models released under the Llama 3 community license, all carrying a 131K context window. The 1B and 3B represent the September 2024 Llama 3.2 generation, trimmed explicitly for edge and on-device deployment; the 8B dates from July 2024 and sits firmly in the hosted inference tier. The 1B model is the weakest of the three by a clear margin. At one billion parameters it struggles with instruction-following and most generation-quality tasks, making it meaningful only as a latency baseline, a proxy for testing on extremely constrained hardware, or a routing-layer triage step where a small percentage of acceptable answers is acceptable. Sub-$0.05 per million tokens at most providers, but you get what you pay for. The 3B delivers acceptable quality for classification, short-form summarization, and content moderation routing. The 131K context window is its standout feature relative to competing 3B models, making it viable for long-document classification that would otherwise require bumping up to the 8B tier. Several platforms price it below $0.10 per million tokens, which matters if you are running volume-heavy batch workloads. The 8B is the practical baseline for teams building conversational or reasoning applications. It handles multi-step instruction-following, lightweight coding tasks, and summarization reliably, at costs that compress with provider competition. General knowledge and tool-calling are meaningfully better than either smaller variant. Pick the 1B only for edge hardware or latency benchmarking. Pick the 3B for high-volume, quality-tolerant batch pipelines where the cost gap to 8B is worth measuring. Pick the 8B for anything that requires coherent multi-turn responses or structured output.