[Gemma 2 2B IT](/models/google--gemma-2-2b-it) and Phi-3 Mini 128K share a sub-4B parameter count but are optimized for opposite ends of the inference constraint spectrum. The critical stat: Phi-3 Mini 128K supports a **128K context window** against Gemma 2 2B's **8K hard cap**. Microsoft also trained Phi-3 Mini on a high-quality synthetic "textbooks" dataset, producing benchmark scores that punch above its weight class. Phi-3 Mini 128K scores approximately 68–70 on MMLU — notably higher than Gemma 2 2B's ~52. On reasoning tasks like GSM8K (math word problems), Phi-3 Mini scores around 82% vs Gemma 2 2B's ~50%. That's a substantial gap for a model in the same size bracket, driven entirely by training data quality rather than scale. Pricing is close: both run $0.02–$0.08/M input tokens at most providers. Phi-3 Mini's backing from Microsoft/Azure means strong availability on Azure AI and similar enterprise-grade endpoints. **Gemma 2 2B IT** fits maximum-throughput short-context pipelines: classification, sentiment tagging, and routing jobs where inputs are compact, latency is critical, and you're optimizing tokens-per-dollar rather than accuracy. **Phi-3 Mini 128K** is the better choice for any task where reasoning quality matters — code explanation, math problem decomposition, or step-by-step instruction following over longer documents. Its 128K window also makes it viable for document-level tasks that Gemma 2 2B cannot handle. Pick [Phi-3 Mini 128K](/models/microsoft--phi-3-mini-128k) if accuracy on reasoning or math tasks matters, or if you need 128K context at the sub-4B tier. Pick Gemma 2 2B IT for raw throughput and lowest possible cost on short inputs.