Both [Gemma 2 2B IT](/models/google--gemma-2-2b-it) and Granite 3.1 2B Instruct sit at the 2B parameter floor, targeting edge inference and high-throughput batch jobs where cost per token is the primary constraint. The sharpest divergence is context window: Gemma 2 2B tops out at **8K tokens**, while Granite 3.1 2B Instruct supports **128K tokens** — a 16× gap that opens Granite to document-level tasks that Gemma 2 2B simply cannot serve. On pricing, both are extremely cheap — sub-$0.05/M input tokens at competitive providers, with Gemma 2 2B often cheaper due to wider provider coverage from Google's open-weights distribution. Throughput at 2B scale is fast on both; Granite's larger context may incur quadratic attention costs at the high end of its window. Benchmark quality at 2B is modest for either model. Gemma 2 2B achieves ~52 MMLU; Granite 3.1 2B lands in the 48–52 range. IBM's Granite series is explicitly enterprise-safety-tuned — content filtering, bias mitigation, and attribution — which matters in regulated industries or internal tooling where policy compliance is audited. **Gemma 2 2B IT** excels at latency-sensitive, high-volume classification and tagging over short inputs: intent detection, label routing, and simple entity extraction where inputs are well under 8K. Its broad availability means you can shop providers aggressively. **Granite 3.1 2B Instruct** earns its spot in enterprise pipelines needing a lightweight model over longer documents — summarizing support tickets, short-form contract review, or internal Q&A where safety guarantees are a compliance requirement. Pick [Granite 3.1 2B Instruct](/models/ibm--granite-3.1-2b-instruct) if you need 128K context or enterprise safety certification. Pick Gemma 2 2B IT for maximum throughput on short-context, cost-sensitive jobs.