The race to Agentic AI just accelerated with o3 and o4-mini

OpenAI just raised the bar with o3 and o4-mini, two new models built to push the limits of reasoning and autonomy.

These models don’t just answer questions.

They plan, reason, call tools, and verify information mid-task, blurring the line between simple language models and true AI agents.

Until now, the idea of agentic AI has been more aspiration than reality. Models could predict text impressively, even simulate thought through chain-of-thought prompting, but they still operated as glorified autocomplete engines, bound to direct outputs.

With o3 and o4-mini, that boundary feels genuinely challenged. We have to ask: Are we actually getting closer to AGI?

What's AGI? Unlike Narrow AI, which is trained for specific tasks (like writing text), AGI would understand and solve any problem, like a human.

Whether you're an AI engineer, researcher, or tech enthusiast, today we'll break down how reasoning models are evolving, what’s happening under the hood of o3 and o4-mini, and what it means for the future of AI.

We’ll cover:

• What’s new in o3 and o4-mini

• How they improve on earlier models like o1 and o3-mini

• How they stack up against other leading reasoning AIs (DeepSeek R1, LLaMA 4, Claude 3.7, Gemini 2.5 Pro)

• How they perform on major benchmarks like MMLU, GPQA, GSM8K, and HumanEval

• What agentic capabilities really mean

• Whether o3 and o4-mini move us closer to AGI (or if the gap remains wider than it seems)

Let's get started.