Problem
Model names and version numbers do not explain which option suits a task or what it will cost.
Solution
Describe each model by the tasks it suits and the trade-offs people can act on. Show differences in speed and cost, supported file types, and how much material it can handle.
Choose a useful default so people can start without studying the list. If the system selects a model automatically, show what it selected and provide an override where available.
When people switch models during a conversation, explain whether the new model can use the existing messages and attachments. Mark the switch in the conversation so changes in behavior have a visible explanation.
Use when
Available models differ in ways people can understand and use to make a choice.
Don’t use when
Two options differ by a benchmark point and nothing a person would notice. Surfacing a choice that can’t be made well is a cost with no benefit, and it invites people to believe they’re getting it wrong.
Trade-offs
A model menu adds decisions that newcomers may not be ready to make. Simple descriptions can leave out details experts need. Long lists become harder to maintain as models change.
Checklist
- Can a non-expert tell which option to pick from the labels alone?
- Is the cost difference visible at the point of choosing?
- Is the context limit stated where it matters, or only in documentation?
- Does the list say which options accept images, audio, or files?
- What happens to the conversation when the model changes mid-thread?
- Is the switch recorded anywhere the viewer can see later?
- If routing is automatic, can the viewer see what was chosen and override it?
- How many options are in the list, and what’s the retirement rule?
- Does the default change without warning, and are people told when it does?
- Is the current selection visible without opening the menu?
Compare
ChatGPT names its tiers and puts a short capability line under each, so the menu is doing the explaining rather than deferring to documentation. Claude ties the choice to the retry control as well as the composer. Switching becomes a way to get a second opinion rather than a setting you change once. Perplexity lets the underlying model come from several vendors. The picker becomes a genuine market choice, and the product a layer over models rather than a face for one. Microsoft Copilot mostly hides the question inside the host application, betting that someone writing a document does not want to make an inference-tier decision first, which is the strongest argument against the pattern existing at all.
Related
Mode switch selects how the assistant works. Usage meter shows the allowance consumed. Regenerate can offer another attempt with a different model.
Composer holds the picker. Knowledge cutoff notice explains limits on the model’s knowledge.