OpenAI o1 Explained: The Reasoning Model
OpenAI released o1, a model that “thinks” before responding. This is different.
Here’s what you need to know.
What is o1?
o1 is OpenAI’s reasoning model. Instead of immediately generating text, it works through problems step-by-step internally before answering.
The difference:
- GPT-4: Generates answer directly
- o1: Thinks → Then answers
You can see it “thinking” — the model takes 10-60+ seconds before responding.
How It Works
o1 uses chain-of-thought reasoning. For complex problems:
- Breaks down the problem
- Works through steps
- Verifies logic
- Produces answer
This internal reasoning isn’t visible, but the time taken is.
When o1 Excels
Math and Logic
Complex mathematical problems. Multi-step reasoning. Proofs.
Performance: Dramatically better than GPT-4 on math benchmarks.
Science Problems
Physics, chemistry, biology reasoning. Complex problem-solving.
Coding Challenges
Algorithmic problems. System design. Complex debugging.
Puzzles and Games
Logic puzzles. Strategic reasoning. Constraint satisfaction.
When Not to Use o1
Simple Questions
“What’s the capital of France?” doesn’t need reasoning time.
Creative Writing
o1 isn’t better at creative tasks. Use GPT-4 or Claude.
Quick Conversations
The delay disrupts conversational flow.
Cost-Sensitive Applications
o1 is more expensive per query.
o1 vs GPT-4
| Task | o1 | GPT-4 |
|---|---|---|
| Complex math | Much better | Good |
| Logic puzzles | Much better | Good |
| Coding algorithms | Better | Good |
| Creative writing | Similar | Similar |
| Speed | Slower | Faster |
| Cost | Higher | Lower |
| Simple questions | Overkill | Fine |
The Two Versions
o1-preview
Full reasoning model. Most capable. Slowest.
o1-mini
Faster, cheaper, slightly less capable. Good middle ground.
How to Access
- ChatGPT Plus/Pro: Available now
- API: Available with limits
Real-World Testing
Math Test
Problem: Complex calculus problem
GPT-4: Partial solution, some errors o1: Complete correct solution, showed work
Coding Test
Problem: Design an algorithm for X
GPT-4: Working solution, not optimal o1: Optimal solution, better reasoning about edge cases
Writing Test
Task: Write a blog post
Both: Similar quality. No o1 advantage here.
Cost Considerations
o1 is expensive:
- More tokens used (reasoning)
- Longer processing time
- Higher per-query cost
Use it for tasks where reasoning matters.
The Implications
For AI Development
This is a new paradigm. More compute at inference time, not just training.
For Users
Different tools for different tasks. Quick questions → GPT-4. Hard problems → o1.
For Complex Problems
Previously impossible queries become possible. Multi-step reasoning without human guidance.
Limitations
Speed
Waiting 30-60 seconds isn’t conversational.
Cost
Premium pricing limits casual use.
Overkill
Most queries don’t need this much reasoning.
Still Not Perfect
Complex enough problems will still fail.
Our Take
o1 is genuinely impressive for reasoning tasks. It’s not better at everything—it’s much better at specific things.
Use o1 for:
- Complex math/science
- Algorithmic coding
- Logic puzzles
- Multi-step reasoning
Use GPT-4 for:
- Everything else
The AI toolkit is expanding. Different models for different needs.
AI is getting smarter in different ways. o1 represents one direction: slower, deeper thinking.