Lesson 812 of 1570
AI model families: reasoning models (o1, o3, R1)
Understand what 'reasoning models' do differently and when to use them.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The big idea
- 2Reasoning Models Like o1 and Claude Thinking — Why They're Slower on Purpose
- 3The big idea
Concept cluster
Terms to connect while reading
Section 1
The big idea
Reasoning models like OpenAI's o-series and DeepSeek's R1 'think' before answering — they generate hidden reasoning, then the answer. They're slower but way better at math, code, and logic.
Some examples
- Use o-series for olympiad math
- Use reasoning models for chess puzzles
- Notice the 'thinking time' before output
- Don't use reasoning models for casual chat
Try it!
Take a hard logic puzzle. Try it with a regular model and a reasoning model. Time both. Note the quality difference vs the time difference.
Section 2
Reasoning Models Like o1 and Claude Thinking — Why They're Slower on Purpose
Section 3
The big idea
Reasoning models like OpenAI's o1, o3, or Claude with extended thinking spend extra inference time generating internal chain-of-thought before answering. They're slower and more expensive — but on math, code, and logic tasks they crush regular models.
Some examples
- o1-preview solves an Olympiad problem GPT-4 can't, but takes 30 seconds to do it.
- Claude with extended thinking catches a subtle bug in a 200-line function others miss.
- o3-mini balances reasoning quality with cost for a daily problem-solving driver.
- DeepSeek R1 brought open-source reasoning into the same league as o1 for many tasks.
Try it!
Take a hard math or logic puzzle. Run it on GPT-4 and on o1 or Claude with extended thinking. Compare.
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