Granite 4.0 H Micro vs Llama 3 8B Instruct

On shared benchmark coverage, MMLU PRO has Granite 4.0 H Micro at 43.5 and Llama 3 8B Instruct at 40.5, with Granite 4.0 H Micro ahead by 3.0 points; Google-Proof Q&A has Granite 4.0 H Micro at 32.1 and Llama 3 8B Instruct at 44.8, with Llama 3 8B Instruct ahead by 12.6 points; HumanEval has Granite 4.0 H Micro at 81 and Llama 3 8B Instruct at 68.2, with Granite 4.0 H Micro ahead by 12.8 points. The largest visible gap is 12.8 points on HumanEval, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.

The capability footprint differs most on JSON / Tool use: Granite 4.0 H Micro, JSON / Tool use: Granite 4.0 H Micro, and structured outputs: Llama 3 8B Instruct. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

For cost, Granite 4.0 H Micro lists $0.02/1M input and $0.11/1M output tokens on the cheapest listed provider, while Llama 3 8B Instruct lists $0.02/1M input and $0.05/1M output tokens on the cheapest listed provider. A 70/30 input-output blend puts Llama 3 8B Instruct lower by about $0.02 per million blended tokens. Availability is 2 providers versus 17, so concentration risk also matters.

Choose Granite 4.0 H Micro when long-context analysis, larger context windows, and lower input-token cost are central to the workload. Choose Llama 3 8B Instruct when provider fit and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.