Gemma 2 9B IT and Llama 3.1 8B Instruct are the two most commonly benchmarked open-weights models in the 8–10B range. The single biggest operational difference: [Llama 3.1 8B Instruct](/models/meta--llama-3.1-8b-instruct) supports **128K context** out of the box; Gemma 2 9B is hard-capped at **8K**. For agentic pipelines or long-document workflows, that gap is decisive. On quality, Gemma 2 9B scores approximately 71 on MMLU vs Llama 3.1 8B at ~68. Both handle structured output and function calling well, but Gemma 2 9B shows slightly stronger instruction adherence on short prompts in third-party evals. Llama 3.1 8B has a larger community fine-tune ecosystem, giving ops teams more fine-tuned variants to choose from without training their own. Pricing is nearly identical — $0.04–$0.12/M input tokens depending on provider and tier. Llama 3.1 8B is deployed on virtually every hosted inference provider, giving it the broadest competitive pricing surface. Gemma 2 9B has strong but slightly narrower availability. **Gemma 2 9B IT** is the better fit for high-throughput, short-context tasks: real-time classification, tool routing, JSON extraction from structured forms, and short-context chat where marginal quality improvements on 8K inputs matter. **Llama 3.1 8B Instruct** handles multi-turn agentic loops, RAG over large retrieved chunks, and any pipeline where the combined prompt and context exceeds 8K tokens. Its ubiquitous provider support also simplifies multi-region deployments. Pick [Gemma 2 9B IT](/models/google--gemma-2-9b-it) if your inputs fit 8K and you want the highest MMLU at this parameter count. Pick Llama 3.1 8B if you need 128K context or want maximum provider flexibility.