Which Copilot or AI Model Do You Prefer for Different Tasks, and Why?

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With so many models available now (Sonnet, Opus, GPT‑style models, code‑focused models, etc.), I’m curious how people actually choose which one to use for different tasks in real life.
For example:

Which model do you prefer for pure coding help (writing functions, refactoring, tests)?

Which model feels best for explanations and learning (understanding code, concepts, errors)?

Do you switch models often, or do you mostly stick with one?

Have you noticed any big differences in speed, quality, or “personality” between them?

It would be really helpful if you could share:

Your typical workflow (editor, language, type of projects)

Which model(s) you pick for that workflow

Any concrete examples where one model clearly worked better or worse than another

I think a thread like this could help new users understand not just the model names, but how people actually use them day‑to‑day with GitHub Copilot and other tools.

[Karrar010]

I would suggest, to mostly use Claude Sonnet for day-to-day coding, as its fast, accurate, and doesn't over-explain. Bump to Opus when something is genuinely complex or architectural. Rarely need to switch beyond that.
For learning Sonnet also holds up well. It meets you at your level instead of dumping a wall of text at you.
Biggest difference I've noticed between models: Claude tends to give better responses, whereas GPT-style models will sometimes hallucinate.
Try a few of these on your actual codebase and see what sticks. Marketing claims don't matter much, your specific use case does.

[prashantkoirala465]

I’ve tried a few different models over time, and honestly, I don’t overthink it. For pure coding (writing functions, refactoring, unit tests), I prefer a balanced coding model like Sonnet or GPT-4.1. They’re fast, practical, and usually give clean, usable code without too much fluff. For 80% of my daily work, that’s more than enough.

When things get tricky like debugging across multiple files, refactoring something large, or thinking through architecture, I switch to something deeper like Opus. It feels better at long reasoning, edge cases, and planning before writing code. It’s a bit slower, but worth it for complicated problems.

For explanations and learning, I like whichever model explains clearly without being overly verbose. Sonnet feels very natural for understanding concepts or errors. GPT-4.1 feels slightly more precise when I care a lot about correctness.

In terms of differences:

Speed: balanced models feel faster and better for iteration.

Quality: deeper models shine in complex debugging and multi-step reasoning.

Personality: some are concise and “diff-style,” others are more conversational. I pick based on what mood I’m in — shipping mode or learning mode.

My typical workflow is VS Code + Copilot, mostly backend/API work and some frontend. I keep one model as my daily driver and only switch if: The answer feels shallow, The task spans multiple files, I need serious debugging help. If I had to give simple advice: start with a balanced model. If it struggles, move up to a deeper one. Don’t switch constantly, switch intentionally.

That’s what’s worked for me day-to-day.