Stop Picking a Favorite: How I Blend GPT, Gemini, and Claude in Real Projects
Artificial intelligence tools evolve so quickly that the old “which model is best?” debate feels dated before you finish writing it. The reality—at least for me—is that no single model is universally better. They’re not interchangeable. Each model has strengths, blind spots, and very specific job roles where it shines.
After a year of building apps, UX prototypes, debugging stubborn issues, studying for OMSCS, and even doing some worldbuilding with AI daily, my philosophy is simple:
You don’t pick “the best model.” You experiment until you find which one fills the gaps in your workflow—or you blend them into a single toolkit.
This post breaks down how I actually use GPT, Gemini, and Claude together in real projects, why each one has value, where each of them fails, and how you can decide which models fit your workflow.
Why I Use Multiple Models Instead of Committing to One
If all you need is quick summaries, small scripts, or basic explanations, any model—free tier included—can handle it.
But once you start:
- building multi-page apps
- debugging deeply nested issues
- designing long-term projects
- writing documentation
- studying technical coursework
- or trying to reason about entire systems
…you begin noticing each model’s quirks and strengths.
I stopped asking “which model is best?” and started asking:
What is this model best at, and how do I make it work for me?
For context, my day-to-day usage is primarily with GPT-5.1, Gemini 3 Pro, and Claude 4.5 Opus/Sonnet—the latest generation models available as of late 2025. Your experience may vary depending on which versions you use, but the general strengths and weaknesses tend to stay fairly consistent across updates.
Here’s my honest breakdown after genuinely using all three.
GPT — A Creative Engine That Sometimes Chases Its Own Tail
GPT is the model I reach for when I want creativity, exploration, or expressive problem-solving. It’s easily the most fun to brainstorm with.
I use GPT for:
- Creative brainstorming
- Documentation drafts
- Worldbuilding and narrative ideas
- UI or UX concept exploration
- Code reviews and lightweight refactoring
For general programming tasks—refactoring a function, cleaning up a file, or explaining a bug—it does a solid job.
Where GPT Struggles
When debugging larger problems, GPT can fall into a frustrating loop:
- You show it a bug.
- It proposes a fix.
- The fix introduces a new issue.
- GPT tells you to undo the previous fix.
- Repeat.
It solves problems locally rather than globally—focusing on the one function you mentioned instead of the system surrounding it.
How I Use GPT Daily
Always in ChatGPT. The interface is clean and straightforward, though it could use:
- bulk chat deletion
- folders or grouping
- hierarchical project organization
For early ideation or whenever I want some creative energy, GPT is still my first stop.
Gemini — Fast, Literal, and Efficient (Especially for Debugging)
Gemini is the most “straight to the point” model of the three. It doesn’t try to impress. It rarely gets poetic. But when I need to get something done, that bluntness becomes a superpower.
I use Gemini heavily for:
- Google ecosystem tasks
(Analytics, reCAPTCHA, OAuth, Firebase) - Identifying root causes of bugs
- Realistic solution paths
- Quick technical checks
- Fast iteration and fact-finding
Gemini gives practical, grounded answers:
“Do A, then B, then C. Here’s why.”
Where Gemini Struggles
Its biggest weakness: context retention.
Sometimes it forgets instructions within the same conversation, even after pinning context.
For example: I’ll tell it explicitly to use Svelte syntax, and two turns later it quietly reverts back to React—even though nothing in the conversation changed.
That forgetfulness becomes a real obstacle when working on multi-step instructions or anything that requires consistency.
It’s also not great at coordinating multi-file, multi-layer projects. For anything requiring global awareness, Claude stays ahead.
Why Gemini Still Matters
It gives realistic expectations without hype. For studying, debugging, or breaking down a complex problem into digestible pieces, Gemini is the most honest guide.
Claude — The Architect and Documentation Powerhouse
Claude is the model that consistently surprises me, especially Claude Code. It may not be flashy, but it’s capable of something the others struggle with:
Holding a coherent mental model of a multi-file project.
That alone makes it invaluable.
I rely on Claude for:
- Architecture planning
- File structure and boilerplate creation
- Full documentation sets
- Large-scale refactoring
- High-level reasoning across complex systems
- Technical writing and schema design
Claude sees the whole picture, not just the function you showed it.
Where Claude Struggles
The power comes with friction:
- The web app often stops mid-response with
“I don’t have enough room to continue.” - Claude Code hits token limits quickly.
- It can resist fine-tuning small details.
- The cost is…not small.
Claude Pro or Team pricing can add up fast. For established teams it's a no-brainer, but for solo developers or students it becomes a careful decision.
Claude’s Standout Strength
If you need:
- full documentation
- system rewrites
- multi-file consistency
- architectural refactors
Claude is simply the best I’ve used.
How I Use All Three Together (Real Workflow)
Instead of forcing one model to do everything, I let each one play a role. My typical flow looks like this:
1. Ideation — GPT (Lead)
I start with GPT to explore:
- themes
- UI direction
- features
- game mechanics
- tone and narrative ideas
GPT is the most playful model, and that helps me define what I actually want to build.
2. Architecture & Documentation — Claude (Lead)
Once I know what I’m building, Claude helps define how.
Claude handles:
- file structure
- API schemas
- logic flows
- initial boilerplate
- architectural sanity checks
- documentation drafts
No other model consistently handles multi-file reasoning this well.
3. Debugging & Reality Checks — Gemini (Lead)
As the project grows:
- Errors appear
- Missed steps surface
- Practical decisions matter
Gemini gives grounded advice, uncovers root causes, and keeps expectations realistic. GPT might be too imaginative here. Claude might over-engineer. Gemini sits in the middle and says:
“Fix this first.”
4. Polishing — All Three
- GPT helps with tone, copy, and creative elements.
- Claude helps with final structuring and documentation.
- Gemini helps verify decisions and sanity-check edge cases.
All three working together feels more natural than trying to transform one into a do-everything model.
The Role These Models Play in Learning (OMSCS, Rust, etc.)
For studying—especially OMSCS—Gemini is surprisingly good. It gives honest timelines, realistic steps, and avoids overly optimistic explanations.
GPT is great for breaking down concepts into digestible pieces.
Claude is excellent for connecting multiple course topics into a coherent understanding.
But if I want a “coach” type voice, Gemini typically gives the most practical advice.
Cost: The Honest Reality
My usage:
- Gemini — included with school access
- GPT — standard ChatGPT subscription
- Claude — Pro plan
From a purely financial perspective:
- Gemini ➝ most cost-efficient
- GPT ➝ middle ground and consistently useful
- Claude ➝ most powerful, but also the one where you feel every dollar
If you’re building with a team, Claude Team or Opus is worth it.
For solo devs or students trying to keep spending sane, GPT + Gemini covers the majority of real-world needs.
Claude becomes the specialist you bring in for large refactors or documentation—not a daily driver.
Final Thoughts: Pick Tools That Fit Your Workflow, Not the Hype
You don’t need every subscription.
You don’t need to follow AI model wars.
You don’t need the “most powerful model” unless your tasks demand it.
Start with your actual workflow:
- What slows you down?
- What energizes you?
- What frustrates you?
- What are you building?
- What are you learning?
- Where do you get stuck?
Then experiment until you find which model fills those gaps.
For me, the blend looks like:
- GPT for creativity and fun
- Gemini for practicality and debugging
- Claude for structure and architecture
Once you stop trying to crown a single winner and start treating these models like teammates with different strengths, everything becomes smoother, faster, and a lot more enjoyable.
Your job isn’t to find “the best AI”—it’s to become a great AI conductor.