We often talk about AI like it’s one big monolith. But the truth is: not all AI models are the same—and understanding the difference is becoming mission-critical for business leaders, government teams, and anyone trying to build a future-ready organisation.

Over the last year, I’ve been working with various models—ChatGPT, Claude, Gemini—and I noticed something that most headlines miss: these models don’t just produce different answers. They think differently.

That subtle difference in how they reason, respond, and research has huge implications for how we use them—and where we shouldn’t.

So let’s break it down.

Three Kinds of AI Models

We can categorise current AI systems into three functional types:

1. Response Models: Fast, Friendly, and Task-Oriented

These are the “chatbots” most people are familiar with. You ask, they answer. Quick, fluent, confident.

ChatGPT 4.0 is the most well-known here. It’s what I call a response model. Great for summarising, drafting content, giving recommendations, or helping you ideate. It’s also now multi-modal—meaning it can work with text, images, and voice (hence the “Omni” in GPT-4o).

It’s like having a super-responsive assistant. But while it’s great at turning instructions into outputs, it doesn’t always “think” deeply through edge cases, ambiguity, or complexity.

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Use case example:

A marketing team uses GPT-4o to rewrite reports, translate internal updates, and even prepare LinkedIn copy. Fast, accurate, helpful. But not the tool to debate brand positioning or challenge strategic assumptions.

2. Reasoning Models: Thoughtful, Reflective, and Context-Aware

Now step into the world of Claude Opus or Gemini Pro 2.5. These models are built to “think”—not just respond. They’re slower, but more deliberate. Better at weighing options, considering nuance, and engaging with complexity.

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These are what I call reasoning models.

If the response model is a fast assistant, this one’s your strategy advisor—the kind that pauses before speaking, asks clarifying questions, and helps unpack layered problems.

Use case example:

In a recent workshop on AI strategy, we used Claude Opus to explore policy options for aged care reform. It didn’t just list the pros and cons—it analysed trade-offs, questioned assumptions, and flagged ethical implications.

3. Research Models: Curious, Connected, and Context-Building

This is the most powerful—and still emerging—category. These models don’t just generate or reason. They also search. They combine internal capabilities with access to external data sources, academic research, real-time web browsing, or internal company documents.

These are your research agents.

Think of them like a cross between a PhD student and a consulting analyst—able to gather, cross-reference, and synthesise information in real time.

Use case example:

A health organisation exploring a new telehealth model used a research-oriented Claude setup combined with RAG (retrieval augmented generation). It scanned internal policy documents, academic papers, and market reports to help shape a business case. What would’ve taken two weeks of research was completed in hours.

Why This Typology Matters

Most organisations I work with are still treating AI as one-size-fits-all. But as this space matures, precision matters.

You wouldn’t use a whiteboard to write code. Similarly, you don’t want a response model handling your strategic planning.

Leaders need to start matching AI models to the nature of the problem:

The Bigger Shift: From Chatbots to Cognitive Tools

AI isn’t just about replacing human tasks—it’s about augmenting human thinking.

Each of these model types represents a way to amplify different cognitive functions:

• Response models extend our execution speed

• Reasoning models support critical thinking

• Research models expand knowledge synthesis

So the key question becomes: What type of thinking do you want to amplify?

What’s Next?

In an upcoming piece, I’ll dive deeper into how I personally use these different models in real client work—from framing aged care strategies to mapping AI transformation plans in professional services.

But for now, I’ll leave you with this:

Understanding AI isn’t just a technical skill. It’s a strategic capability. And the leaders who learn to differentiate between model types will be the ones who create real, lasting value.

Suhit Anantula is a Strategy Designer and Systems Thinker helping leaders across business, government, and the social sector build future-ready organisations. As the founder of The Helix Lab, he works at the intersection of strategy, systems, and AI; enabling organisations to design smarter strategies, embed sustainable systems, and lead transformation with clarity and confidence. (www.suhitanantula.com)