Llama 3 8B Instruct vs o3

On shared benchmark coverage, Google-Proof Q&A has Llama 3 8B Instruct at 44.8 and o3 at 87.7, with o3 ahead by 42.9 points; HumanEval has Llama 3 8B Instruct at 68.2 and o3 at 96.7, with o3 ahead by 28.5 points. The largest visible gap is 42.9 points on Google-Proof Q&A, 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 reasoning mode: o3 and code execution: o3. Both models share structured outputs, 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, Llama 3 8B Instruct lists $0.03/1M input and $0.04/1M output tokens, while o3 lists $1/1M input and $4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3 8B Instruct lower by about $1.87 per million blended tokens. Availability is 17 providers versus 3, so concentration risk also matters.

Choose Llama 3 8B Instruct when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose o3 when coding workflow support and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.