A food-truck permit tester needs an LLM for two jobs: (a) rewrite one user story into a simple case table, and (b) justify a risk-based case order from several competing constraints. Which pairing best matches model type to task?
Select an answer to reveal the explanation.
Short Explanation
Think of instruction-tuned models as careful clerks who follow a clear recipe, and reasoning models as planners who weigh several rules at once. Rewriting one story into a table is mostly follow-the-instructions work, while ranking cases under many constraints needs deeper multi-step thinking. Match the model to that demand.
Full Explanation
Instruction-tuned (non-reasoning) models excel when the tester supplies a clear format and the work is mainly following instructions. Multi-constraint planning—such as ordering cases by risk under several competing factors—benefits from a reasoning model that can chain inferences. Selecting the heavier reasoning model for a simple rewrite wastes cost and latency; selecting only an instruction-tuned model for hard prioritization often yields shallow justification.