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Reasoning models 'think' before answering — slower and pricier, but way better on math, code, and logic.
OpenAI's o1, o3, Claude with extended thinking, Gemini Thinking, DeepSeek R1, and Grok with reasoning — these all share a trick. Before answering, they generate a long internal 'thought' process. You don't see most of it. The result: way better on math, science, and tricky code. The cost: 10–100x more tokens and longer waits. Use them when accuracy matters.
Run the same competition-math problem on a regular model and a reasoning model. Compare answer and time.
8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-builders-models-reasoning-models-explained-r7a8-teen
What is the main idea of "Reasoning Models (o1, o3, Claude Thinking) vs Regular Chat Models"?
Which concept is most central to "Reasoning Models (o1, o3, Claude Thinking) vs Regular Chat Models"?
Which use of AI fits this topic best?
What should a careful learner remember about "The rule"?
You want to use AI after this lesson. What is the safest next step?
How should AI output about reasoning models be treated?
Name one way to verify an AI answer about reasoning models.
Which action would help you apply "Reasoning Models (o1, o3, Claude Thinking) vs Regular Chat Models" responsibly?