Master the terminology behind OpenAI's o3 reasoning models.
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At standup, a dev notices billed tokens exceed visible output on o3. What accounts for the difference?
Reasoning models like o3 consume hidden reasoning tokens for internal thinking that are billed but not shown in the final output. They explain why usage exceeds the visible response length. Budgeting must account for them.
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During a design review, the team wants to cap how much o3 thinks. Which control do they tune?
A thinking budget (often surfaced as reasoning effort) limits how many reasoning tokens the model may spend. Lower budgets reduce latency and cost; higher budgets improve hard-problem accuracy. It is the main lever for the cost/quality tradeoff.
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In a code review, a dev sets reasoning effort to low/medium/high. What does this control?
The effort levels parameter (low, medium, high) tells o3 how much reasoning to apply before answering. Higher effort spends more reasoning tokens for tougher tasks. It is a coarse but convenient knob distinct from sampling settings.
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During a PR review, someone asks when to pick o3 over GPT-4o. What is the key difference?
Compared with GPT-4o, o3 is optimized for deliberate, multi-step reasoning on complex problems at higher latency and cost. GPT-4o is often better for fast, general chat and low-latency tasks. Choose based on whether the task needs deep reasoning.
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An incident report shows huge bills from a high-effort reasoning loop. What is the first tuning step?
Reducing the reasoning effort or thinking budget directly cuts hidden reasoning tokens and thus cost. You trade some accuracy on the hardest cases for big savings on routine ones. Right-sizing effort per task is the standard fix.