What Is the Difference Between deep^2 and deep^3 Reasoning in Amp
When I first started using Amp’s deep mode, I noticed there were different reasoning levels: deep^2 and deep^3. I wasn’t sure which one to use or what the actual difference was. After experimenting with both, I’ve learned when each level makes sense and how they affect my workflow.
The Problem: Understanding Reasoning Levels
Amp’s deep mode isn’t a single setting—it offers two reasoning intensities. The problem? The documentation doesn’t clearly explain what each level does or when to use it. Should I always use the highest reasoning? Will deep^3 be too slow for interactive work?
These questions matter because the reasoning level you choose affects both the quality of responses and how quickly you get them.
What Are Reasoning Levels?
Reasoning levels in Amp determine how deeply the model “thinks” before responding. Think of it like this: deep^2 is like quickly thinking through a problem, while deep^3 is like sitting down and really working through all the implications.
| Level | Default? | Reasoning Depth | Best For ||----------|----------|-----------------|-----------------------------------|| deep^2 | Yes | Standard | Most coding tasks, quick fixes || deep^3 | No | Very High | Complex refactoring, architecture |The key insight: deep^3 doesn’t just mean “more”—it means the model explores more paths, considers more edge cases, and generally produces more thorough responses.
How I Tested Both Levels
I ran a series of tests with GPT-5.4, Amp’s latest model. I worked on:
- Simple refactoring - Renaming variables, extracting functions
- Bug fixes - Tracking down obscure issues
- Architecture decisions - Designing a new module structure
- Complex debugging - Multi-file issues with unclear causes
Here’s what I found:
deep^2 (Default)
For most coding tasks, deep^2 was perfectly adequate. It’s fast, responsive, and handles:
- Writing new features
- Code reviews
- Documentation
- Simple to medium complexity refactoring
The response time felt nearly instant, making it great for interactive pair-programming sessions.
deep^3 (Higher Reasoning)
When I switched to deep^3, I noticed:
- Better analysis - The model caught edge cases I hadn’t considered
- More thorough explanations - It explained why certain approaches were better
- Still responsive - With GPT-5.4, deep^3 didn’t feel sluggish
What surprised me was that GPT-5.4 at deep^3 still felt snappy enough for interactive work. According to Amp’s announcement, users prefer GPT-5.4 at deep^3 because it “takes steering better than GPT-5.3-Codex”—meaning you can guide its reasoning more effectively.
When to Use Each Level
Based on my experience, here’s a practical guide:
| Task Type | Recommended Level | Why ||------------------------|-------------------|----------------------------------------|| Writing new code | deep^2 | Fast iteration, good enough reasoning || Bug hunting | deep^3 | Need to consider all possibilities || Architecture decisions | deep^3 | Better long-term thinking || Code review | deep^2 | Quick feedback loop || Complex refactoring | deep^3 | Need to understand all implications || Quick questions | deep^2 | No need for deep analysis |How to Switch Between Levels
Switching is straightforward: press Opt-D to toggle between deep^2 and deep^3.
I’ve gotten into the habit of starting with deep^2 for most tasks, then switching to deep^3 when:
- The initial solution feels incomplete
- I’m dealing with a particularly tricky bug
- I need architectural guidance
- The code I’m working on has complex interdependencies
Common Mistakes to Avoid
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Always using deep^3: This is overkill for simple tasks. You’re burning compute for no real benefit.
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Never trying deep^3: You’re missing out on genuinely better reasoning for complex problems.
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Assuming deep^3 is too slow: With GPT-5.4, this isn’t true anymore. The model handles high reasoning efficiently.
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Treating them as “better/worse”: They’re different tools for different jobs. deep^2 isn’t “worse”—it’s optimized for speed and interactive work.
The Trade-off Matrix
Here’s how I think about the trade-offs:
| Aspect | deep^2 | deep^3 ||------------------|---------------------|-----------------------|| Speed | Faster | Slightly slower || Depth | Standard | Very high || Edge cases | May miss some | Catches most || Best use case | Interactive work | Complex problems || Compute cost | Lower | Higher |My Workflow Now
I’ve settled into a rhythm:
- Start with deep^2 for any new task
- Evaluate the response - Is it thorough enough?
- Switch to deep^3 if:
- The problem is genuinely complex
- The initial answer feels surface-level
- I’m making architectural decisions
- Switch back to deep^2 for follow-up questions and refinements
This approach gives me the best of both worlds: speed when I need it, depth when it matters.
Key Takeaways
- deep^2 is the default for a reason—it handles most tasks well
- deep^3 offers maximum reasoning and is now viable for interactive work with GPT-5.4
- The choice matters for both response quality and resource usage
- Toggle with Opt-D to find the right level for each task
The bottom line: don’t overthink it. Start with deep^2, switch to deep^3 when you need more depth. With GPT-5.4’s efficiency, you have the flexibility to use the right tool for each situation.
Final Words + More Resources
My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me
Here are also the most important links from this article along with some further resources that will help you in this scope:
Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!
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