Using only one AI model in 2026 is like using only one search engine in 2010.
Here's a confession: I used to be a "one AI" person. I found the one that felt right, stuck with it, and ignored the rest.
That changed when I started doing serious work with AI — not just "write me an email," but real tasks: debugging complex code, analyzing research papers, generating documentation, building creative assets.
Every model has a personality. And every personality has a superpower.
The Lineup
| Model | What It's Best At | When I Reach For It |
|---|---|---|
| GPT-4o | Brainstorming, creative writing, quick Q&A | First draft of anything |
| Claude (Opus/Sonnet) | Deep analysis, long-form, nuanced reasoning | Research summaries, detailed reviews |
| Gemini 3.5 Flash | Agentic tasks, coding, multimodal | Parallel tasks, code debugging, PDF analysis |
| DeepSeek | Technical/coding work | Complex algorithms, system design |
| Grok | Real-time info, unconventional takes | Breaking news, contrarian viewpoints |
Yesterday Google dropped Gemini 3.5 Flash at I/O 2026. Today I'm reading about Karpathy joining Anthropic. This week Mistral acquired Emmi AI to build their AI stack.
The pace of change means the "best model" changes weekly.
But here's the thing most people miss: having the right model is only half the battle. The other half is keeping track of what you've learned from each one.
The Hidden Cost of Multi-Model Work
When you work across multiple AI platforms, you accumulate value in each one:
- A brilliant debugging session in DeepSeek that solved a gnarly race condition
- Claude's analysis of a research paper that shaped your project direction
- GPT-4o's brainstorming that led to a creative breakthrough
- Gemini's multimodal analysis that extracted insights from a complex PDF
And then you close the tab and lose it all.
I've lost count of how many times I've thought "I know I asked an AI about this before..." and spent 20 minutes searching through browser history.
What Actually Works
After months of trial and error, here's my system:
1. Export valuable conversations immediately
Not "later." Not "when I have time." Right when something useful comes up. Takes 2 seconds, saves 20 minutes later.
2. Use formats that match your workflow
- Markdown for Obsidian/Notion personal knowledge bases
- PDF for sharing with team or clients
- DOCX for incorporating into reports
- JSON for programmatic processing
- Clipboard for quick pasting into docs
3. Name files consistently
My pattern: YYYY-MM-DD-topic-platform.ext
Example: 2026-05-19-race-condition-debugging-deepseek.md
4. Organize by project, not by platform
Don't put all ChatGPT exports in one folder. Put all "Project X" exports together, regardless of which AI produced them. That's how you actually find things later.
The Tool Part
I use a Chrome extension called XWX AI Chat Exporter that covers all 5 platforms I use. Single interface, one-click export in any format. Handles code blocks, LaTeX formulas, images — the whole thing.
But honestly, pick any tool that works. Even manual screenshots are better than nothing. The system matters more than the tool.
One More Thing: Your AI Conversations Are Your Thinking Process
This is the part that really clicked for me: my AI conversations aren't just chat logs. They're my thinking process, externalized.
When I talk through a problem with Claude, that's my reasoning. When I debug with DeepSeek, that's my problem-solving approach. When I brainstorm with GPT-4o, that's my creativity amplified.
If you wouldn't throw away your own notes, don't throw away your AI conversations.
Your Turn
What's your AI workflow? Are you a one-model person or a multi-model person? How do you keep track of important AI conversations?
I'm always looking to learn what's working for other people. Drop a comment — I read every one.
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