Reasoning model
A language model trained or configured to think step-by-step before answering, often visibly. Better at hard problems, slower and more expensive than standard models.
A reasoning model is a language model that thinks before it answers, generating internal “thinking” or “reasoning” tokens that the final response builds on. OpenAI’s o-series (o1, o3), Anthropic’s Claude with extended thinking, and Google’s Gemini Thinking are all examples.
The trade-off: reasoning models are slower and more expensive per response than standard models, but they’re meaningfully better on hard problems (multi-step math, complex code refactors, intricate planning).
When to use a reasoning model
- Complex coding tasks with multi-file reasoning
- Math, logic, planning problems
- Research that requires weighing multiple competing factors
- Anything where a wrong answer is more expensive than a slow answer
When NOT to use a reasoning model
- Routine drafting, summarising, formatting (overkill)
- Anything user-facing where latency matters (reasoning adds seconds-to-minutes per response)
- Cost-sensitive workflows (reasoning tokens can 5-10x your output cost)
How it shows up in Claude Code
Claude Code supports “extended thinking” on supported models. You can toggle it explicitly via the model picker, or let the harness decide based on the task. Thinking tokens are billed but not displayed by default.
For most Australian SMB workflows we run, standard Sonnet 4.6 without extended thinking is the right default. Switch to thinking-enabled Opus for multi-file refactors or genuine reasoning challenges.
Watch for: extended thinking blowing your budget
A reasoning-enabled session can spend 10x more output tokens than a standard one. Set token caps. Audit your top 5 sessions monthly.
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