[Gemma 2 2B IT](/models/google--gemma-2-2b-it) and Llama 3.2 3B Instruct are the two most commonly deployed sub-4B models for latency-constrained inference. The immediate trade-off: Llama 3.2 3B offers **128K context** vs Gemma 2 2B's **8K ceiling**, and the parameter difference (3B vs 2B) gives Llama a measurable quality edge. On benchmarks, Llama 3.2 3B scores approximately 58 on MMLU compared to Gemma 2 2B's ~52 — a 6-point gap that is meaningful at this scale. Llama 3.2 3B also benefits from Meta's instruction-tuning pipeline, which tends to produce better structured-output compliance and tool-calling accuracy than Gemma 2 2B IT's lighter instruction fine-tune. Pricing at this tier is very competitive — both typically run $0.02–$0.06/M input tokens. Gemma 2 2B is often fractionally cheaper and has strong availability across smaller inference providers and local runtimes. Llama 3.2 3B is essentially ubiquitous across every major hosted provider. **Gemma 2 2B IT** is the right pick for extreme-throughput workloads over short inputs: spam filtering, micro-classification, and real-time tagging pipelines where you need maximum tokens-per-second at minimum cost and inputs never exceed a few thousand tokens. **Llama 3.2 3B Instruct** is better for any task requiring reliable instruction following over longer inputs — summarizing user sessions, lightweight agentic steps, or RAG retrieval over 8K+ document chunks. Pick [Llama 3.2 3B Instruct](/models/meta--llama-3.2-3b-instruct) if you need 128K context, better structured-output compliance, or slightly higher MMLU quality. Pick Gemma 2 2B IT if raw throughput and rock-bottom cost on short inputs are the only metrics that matter.