Lesson 1293 of 2116
Reasoning Models (o-series, Claude Extended Thinking, Gemini Deep Think): When the Extra Tokens Are Worth It
When to spend 10x the tokens on a reasoning model — and when a normal model is fine.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2Reasoning Models in Depth: o1, o3, Claude Opus, DeepSeek R1
- 3The premise
- 4AI model families: reasoning models — when the extra latency is worth it
Concept cluster
Terms to connect while reading
Section 1
The premise
Reasoning models trade cost and latency for accuracy on hard problems — they are wasted on easy ones and indispensable on a narrow class.
What AI does well here
- Solve multi-step math and logic problems beyond standard models
- Plan long agentic tasks before executing
- Catch their own errors via internal critique
- Outperform standard models on competition-style problems
What AI cannot do
- Justify their cost on simple classification or extraction
- Stay fast — expect 5-30s latency vs. 1-2s
- Replace tool use for tasks needing external knowledge
Key terms in this lesson
Section 2
Reasoning Models in Depth: o1, o3, Claude Opus, DeepSeek R1
Section 3
The premise
Reasoning models are great for hard problems and overkill for easy ones — pick deliberately.
What AI does well here
- Solve multi-step math, planning, and code generation problems.
- Self-correct mid-reasoning when given budget.
- Surface uncertainty better than non-reasoning models.
What AI cannot do
- Justify cost on simple summarization or chat tasks.
- Always be faster than smaller models.
Section 4
AI model families: reasoning models — when the extra latency is worth it
Section 5
The premise
Reasoning models (those that compute internal traces before answering) excel at multi-step problems and pay a heavy latency cost. Use them where the latency budget allows and the task rewards depth.
What AI does well here
- Solve multi-step math, logic, and planning problems more reliably
- Decompose complex tasks into intermediate steps
- Catch their own mistakes mid-trace
What AI cannot do
- Beat fast models on simple chat or lookup latency
- Hide the latency cost from a user-facing UI without streaming
- Justify the cost on tasks that don't reward reasoning
End-of-lesson quiz
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