---
title: "Choosing commercial or open-weight models"
course: "AI103 · Prompting that works"
unit: "Building and choosing LLM systems"
lesson: 27 of 35
source: https://open.maestrocollege.edu/prompting-that-works/choosing-commercial-or-open-weight-models
publisher: Maestro College
---

# Choosing commercial or open-weight models

Lesson 27 of 35 in **Prompting that works** (AI103), unit "Building and choosing LLM systems". An open lesson from Maestro College.

## What the learner will be able to do
- Distinguish closed (proprietary) commercial models from open-weight models, knowing that either can be used through a hosted API but only open weights can be downloaded and self-hosted, and identify what each means for privacy (where your data is sent and stored) and why open-weight is not automatically free, private, or fully open source.
- Identify what a public benchmark or leaderboard measures and why a high rank does not prove a model fits your task.
- Understand that no model is best for every task, and for a described use case compare at least two closed commercial models and one open-weight model on capability, modality, reasoning mode, latency, privacy, deployment, model size (parameter count), and token cost, justifying a recommendation in 3-5 sentences.

## Teaching notes
Have the learner check current specs, prices, and leaderboard ranks on provider pages or a comparison site, since names and prices change often.

## Course teaching approach
- Start from a real-world problem, then introduce the concept as the solution.
- Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning.
- Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks.
- End with one fun closed question about the topic.

## How to use this document
You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates.

## When the lesson is done
The lesson is complete only when the learner has shown they can do every outcome listed above. Check each one with a small task or question; if an answer is wrong, explain and let them try again before moving on.
When every outcome is covered:
1. Say clearly: "Lesson 27 complete: Choosing commercial or open-weight models."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Retrieval-augmented generation (RAG)" (lesson 28 of 35), and give the learner its link: https://open.maestrocollege.edu/prompting-that-works/retrieval-augmented-generation-rag.md. Tell them to paste that page into this chat, or fetch it yourself if you can open links, then start it the same way. Do not start teaching the next lesson's topic until the learner has it.

Previous lesson: Modalities and reasoning modes
Next lesson: Retrieval-augmented generation (RAG)