You've learned how AI "thinks" through patterns. But not all AI models work the same way! Some are built for deep, complex reasoning while others excel at specific tasks.
OpenAI's o3 and Claude Opus are reasoning models. GPT-4o and Gemini Flash are standard models.
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Can you describe a study situation where deep reasoning might be needed?
Reasoning models like o3 and Claude Opus break down complex problems step-by-step. They literally show their "thinking" process as they work.
You'll notice they take longer - sometimes minutes instead of seconds - but deliver deeper analysis.
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What's a complex academic problem that would be worth waiting minutes for an answer?
Here's how to spot a reasoning model: it takes time, costs more per use, and shows intermediate steps like "Let me think about this..."
For example, ask o3 to analyze why your research hypothesis failed, and it'll walk through methodology, data quality, variable relationships, alternative explanations.
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Have you ever seen an AI show its step-by-step thinking process?
Standard models like GPT-4o and Gemini Flash give quick, direct answers. They're perfect for routine academic tasks that follow patterns.
Ask GPT-4o "Give me some ideas for a presentation topic" and you get an instant, accurate response without lengthy analysis.
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What do you normally ask your favorite AI tool?
Use reasoning models for complex questions like: "Why did my experiment produce unexpected results?" or "How should I approach this research paper?"
The key difference: complex problems vs. routine tasks.
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Which model type would you use to write a standard email to your professor?
