# W3: Model selection scorecard

How Large Language Models Work: Foundations for Choosing and Applying LLMs at Work (FAST-14) · Lab 3: Model selection scorecard

Name: ______________________  Date: ____________

| | Frontier hosted | Small hosted | Local open-weight |
| --- | --- | --- | --- |
| Fields correct / 50 | | | |
| Avg. seconds per letter | | | |
| Avg. input / output tokens | | | |
| Cost per 1,000 letters | | | (hardware note) |
| 40-page letter: fits? middle answer right? | | | |
| License / trains on inputs? / retention / location | | | |
| Main weakness seen | | | |

**Recommendation:** model, settings (such as temperature), the control you would add, and what evidence would change your mind.

**Completion criteria**

- The 10-item answer key exists before any model is run.
- All three models are scored on the same items.
- Cost per 1,000 letters is shown with the formula.
- Data terms are recorded for each model.
- The recommendation cites the trainee's own scores, not a leaderboard.