Two 8B models at nearly identical price points — most providers quote $0.05–0.15/1M tokens for both — so the decision comes down to training focus, not cost. [Llama 3.1 8B Instruct](/models/meta--llama-3.1-8b-instruct) has a substantially larger provider ecosystem and scores around 73% on MMLU versus Granite 3.1 8B's ~72%, a margin that rarely changes outcomes but does show up in general-reasoning evals. Llama 3.1 8B also ships with a 128K context window; [Granite 3.1 8B Instruct](/models/ibm--granite-3.1-8b-instruct) supports up to 128K as well, so context length is not a differentiator here. Granite 3.1 8B Instruct pulls ahead on enterprise IT use cases. IBM optimized this model for RAG over structured enterprise documents — think policy PDFs, IT runbooks, support knowledge bases — and on function-calling tasks within IBM's tool ecosystem. Internal benchmarks on code-related classification and API-call generation show Granite 3.1 8B measurably outperforming vanilla Llama 3.1 8B, particularly on enterprise domain terminology. Llama 3.1 8B Instruct is the stronger default for general-purpose applications. Its training breadth, larger available fine-tune community, and wider provider competition make it the lower-risk starting point. For agentic pipelines that mix domains — web retrieval plus coding plus summarization — Llama 3.1 8B's generalist tuning holds up better across the full chain. **Pick Granite 3.1 8B Instruct** if your workload centers on enterprise IT documents, IBM tool integrations, or structured RAG with domain-specific terminology. **Pick Llama 3.1 8B Instruct** for general-purpose agentic or chat workloads where ecosystem breadth and fine-tune availability matter.