DeepSeek, Qwen, Mistral, and Open-Source Models · Lesson 2
Where Open-Source Really Wins
Scenarios where open-weight models are already no worse than the flagships.
Practical exercise
What to do after this lesson
Take your recurring scenario (form processing / email classification / field extraction). Run it through an open model. Compare on quality and cost.
Task grader
Take your recurring scenario (form processing / email classification / field extraction). Run it through an open model. Compare on quality and cost.
Ready-to-use prompt
Template for this lesson
Copy and adapt to your context. Text in angle brackets should be replaced.
Take a classification task: - Input format: <…> - Desired output: <…> - Categories: <…> Do this for every row. If a category is ambiguous — mark "uncertain" instead of guessing.
Prompt sandbox
Common mistakes
What people get wrong
- They use a huge flagship for a cheap simple task.
- They try to get GPT-class conversation from a 7B model.
- They don't compute the economics of batch processing.
Pro tips
What works but no one documents
- Build a simple benchmark: 100 examples with reference answers — and compare open vs flagship.
- For classification, 7B-13B models are enough.
- For fine writing — keep a flagship.
When to use
Narrow recurring tasks, batch, privacy.
When not to use
A universal 'help me' in complex scenarios.
Quiz — 2 questions
1.Where are open-weight models usually equal to or stronger than the flagships?
2.When is it better to stay with a flagship?
Answered: 0 of 2
Discussion
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