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Model switching (when to use which model)

What this is​

Many AI tools let you pick which “model” does the work: some are fast and cheap, some are better at tricky thinking, and some are best for code. Picking the right one saves time and reduces mistakes. This page is tool-agnostic — the same ideas work no matter which editor or provider you use.

Three buckets​

Think of models in three buckets (your tool may use different names):

  • Fast model — Cheap and quick. Good for simple, repetitive tasks. Use when speed and cost matter more than deep thinking.
  • Strong reasoning model — Better at multi-step logic, planning, and “figuring it out.” Use when the task is fuzzy or the answer isn’t obvious.
  • Best coding model — Best at code: structure, correctness, and staying true to your repo. Use when the code has to be right and maintainable.

Which tasks map to which bucket​

Use the fast model for:

  • Quick questions (“What does this function do?”).
  • Mechanical edits (rename a variable, fix a typo, add a single import).
  • Copy or text rewrites (button label, error message, comment).
  • Simple, repeatable changes where the right answer is obvious.

Use the strong reasoning model for:

  • “Why is this bug happening?” or “What’s the best way to do X?”.
  • Planning or breaking down a feature into steps.
  • Choosing between design or architecture options.
  • Tasks where you need the AI to reason, not just pattern-match.

Use the best coding model for:

  • Writing or changing real code that will ship (new feature, refactor, bug fix).
  • Anything that touches security, auth, or sensitive data.
  • When the AI must follow your repo’s patterns and not invent paths or APIs.
  • Review or cleanup of existing code.

Rule of thumb (two lines)​

If it must be correct, not just plausible — switch up.
Use a stronger or best-coding model when wrong answers are costly (e.g. wrong code, wrong architecture, invented APIs).

If it’s simple and repetitive — switch down.
Use the fast model for mechanical edits and copy changes so you save time and cost.

“Switch up” warning signs​

You’re on too weak a model when you see:

  • Plausible-but-wrong outputs — The answer sounds good but is wrong (wrong API, wrong assumption, wrong file). Switch to a stronger or best-coding model.
  • Invented paths or APIs — The AI suggests files, endpoints, or names that don’t exist in your repo. Switch up and give a clear Context Pack; consider the best coding model for code work.
  • Repeated misunderstanding — You correct the AI and it keeps making the same kind of mistake. Try a stronger model (or a context reset) before adding more messages.

When you see these, switch to a stronger reasoning model for planning, or to the best coding model for code.

“Switch down” cases​

You can use the fast model when:

  • Mechanical edits — Rename, reorder, add a missing import, fix a typo, change a constant.
  • Copy rewrites — Update button text, error messages, comments, or docs in one place.
  • Simple, obvious tasks — The change is clear and low-risk; the fast model is enough.

Switching down saves time and cost. If the fast model starts making mistakes or inventing things, switch back up.

Common mistakes​

  • Using the fast model for important code (e.g. auth or refactors). Wrong code is expensive; use the best coding model.
  • Using the best coding model for every tiny edit. Use the fast model for mechanical or copy-only changes.
  • Ignoring plausible-but-wrong or invented paths. Those are signs to switch up (or reset context), not to keep explaining in the same chat.