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Use a reasoning model when a task is ambiguous, involves several dependent steps, or requires planning and synthesis. Use a standard chat model for clearly specified work—such as routine rewriting, extraction, or classification—when speed and cost matter. These are practical tendencies, not guarantees: the right choice is the model and configuration that meets your quality needs on your actual tasks.
What distinguishes the two model types?
Reasoning-oriented models are suited to spending more effort on complex problem-solving, planning, and weighing relationships across information. Standard chat models are often a better fit for bounded instructions that mainly require executing a known task. The distinction is not a universal ranking: OpenAI’s Reasoning best practices describes the families as different tools, not one as categorically better.
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This guidance reflects OpenAI’s API documentation, not a cross-provider standard. Other providers may define these categories, controls, and trade-offs differently.
Which type should you try first?
| Workload | First option to test | What to check |
|---|---|---|
| Short, clear rewrite, extraction, or routine classification | Standard chat model, or low reasoning effort where supported | Accuracy on edge cases, latency, and cost at your expected volume |
| Ambiguous request or competing interpretations | Reasoning model | Whether it handles uncertainty well, asks useful clarifying questions, and succeeds on the task |
| Multistep planning, complex analysis, or a decision spanning documents | Reasoning model | Correctness, missed dependencies, and whether longer processing adds value |
| Repeated workflow with many simple subtasks and occasional difficult decisions | Hybrid planner/doer workflow | End-to-end success, handoff overhead, tool failures, latency, and total cost |
This is a starting point, not a substitute for testing. OpenAI recommends experimenting on the workflow and comparing task success, latency, token use, and cost per successful task in its Model selection guidance.
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When does a hybrid workflow make sense?
If a task can be separated into a difficult planning decision and clearly scoped execution steps, try using a reasoning model to plan or route the work, then a faster standard model to carry out those steps. OpenAI describes this planner/doer pattern as a successful approach and notes that workflows may combine model families in its reasoning guidance.
Evaluate the whole chain, not just the individual calls. A planning step may add handoffs, latency, or failure points; the hybrid is worthwhile only if it performs reliably and improves the overall trade-off against a single-model route.
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How should you compare models fairly?
- Choose representative tasks. Include ordinary inputs and the edge cases that matter in real use.
- Define success in advance. Specify required accuracy, completeness, formatting, acceptable latency, and the consequences of an error.
- Run matched comparisons. Use the same tasks, prompts, tools, input context, and output requirements for each candidate and setting.
- Inspect failures as well as scores. Record task success and failure types, then track latency and total token use, including reasoning tokens if exposed.
- Calculate cost per successful task. For a hybrid, include every model call and handoff; compare end-to-end results with a single-model setup.
- Select the least costly, fastest setup that meets your quality threshold. Reassess when the workload or model changes.
OpenAI’s API deployment checklist recommends evaluating representative tasks and tracking task success, latency, token use, and cost per successful task. There is no general figure in this guidance establishing that reasoning models are always faster, cheaper, or more accurate.
How does reasoning effort affect the choice?
In OpenAI’s API, supported reasoning-effort values vary by model. Its Reasoning models documentation describes lower effort as favoring speed and lower token use, while higher effort allows more complete work and can take longer or cost more. The documentation associates lower effort with tasks such as extraction, routing, classification, and routine rewriting; medium or high effort with planning, diagnosis, complex reasoning, or code; and the highest settings with tasks where evaluation justifies the extra time and cost.
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Check the documentation for the specific model before choosing a setting; not every model supports every value. API effort controls should not be assumed to map directly to settings in consumer chatbots.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is the practical rule?
Start with task shape, then test: try a standard model for clear, routine execution and a reasoning model for ambiguity, planning, or synthesis. Keep the more capable or higher-effort option only when matched evaluations show that its improvement is worth its added latency and cost. As OpenAI puts it in its deployment checklist, “Choose the model that performs well on representative tasks rather than routing every request to the most capable model.”
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Model names, availability, settings, and prices change. Consult current documentation for the provider and model you intend to use, then validate the choice against your workload.
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