Stage 2 · Lesson 1 of 6
Reasoning models and fast models
A writes hidden working () before its final answer, a bit like a student using scrap paper. That helps on problems with many steps: maths, planning, debugging, careful reading of messy documents. A fast model answers straight away.
It is cheaper and quicker, and for simple tasks such as classifying a message or rewriting a sentence it is often just as accurate. In 2026 the line is blurred: many current models can do both, and you choose how much they think with a setting.
The reasoning tokens are billed, usually at the output price, and they add seconds of waiting. So reasoning is not a free quality upgrade. It is a trade you measure.
A question to check yourself, an AI practice session and 2 cheat sheet entries come with the full roadmap. Unlock
Resources for this stage
Start with· follow one of these
- CoursePaidGrokking Prompt Engineering for Software Engineers
Design Gurus · Beginner · Subscription
Write prompts with clear rules and formats, so you can test instruction following properly
- VideoFreeDeep Dive into Large Language Models like ChatGPT
Andrej Karpathy · Intermediate
Free: a clear explanation of how reasoning models are trained and why thinking helps
Practise
Keep handy
Also worth your time
How reasoning effort works in practice and where the reasoning tokens show up in usage
- Large Language Model CourseGo deeper
Free: go deeper into how models are trained, which explains where reasoning comes from