## Llama 3.2 1B Instruct vs Llama 3.2 3B Instruct Both models target edge and on-device deployment, but they occupy distinct operating points. [Llama 3.2 1B Instruct](/models/meta--llama-3.2-1b-instruct) costs roughly $0.04–$0.06/1M tokens on hosted providers; [Llama 3.2 3B Instruct](/models/meta--llama-3.2-3b-instruct) runs $0.06–$0.10/1M tokens — a 1.5–2× premium for roughly 15–20% higher accuracy on instruction-following benchmarks (IFEval, MT-Bench). The 1B fits comfortably in ~2 GB of RAM at 4-bit quantization; the 3B needs ~2.5 GB, which matters on constrained devices. Throughput on shared cloud inference is fast for both — expect 200–400 tokens/sec for the 1B and 150–300 tokens/sec for the 3B — making latency a non-issue for most real-time text applications. **Where 1B wins:** Keyword extraction, intent classification, simple slot-filling, and on-device inference on phones or microcontrollers with tight memory budgets. When you need sub-100 ms responses and the task is well-structured enough that a smaller model doesn't struggle, the 1B delivers at the lowest possible cost. **Where 3B wins:** Multi-turn chat, short summarization, lightweight code completion, and any task requiring coherent paragraph-length outputs. The 3B noticeably reduces repetition and hallucination on free-form generation tasks compared to the 1B, justifying the modest price increase. Pick the 1B if you're optimizing for inference cost or device memory and your task is classification or structured extraction. Pick the 3B if output quality degrades visibly with the 1B and you can tolerate a 2× cost increase.