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Reasoning Effort Isn’t a Universal Setting
OpenAI describes reasoning effort as a control over how much a model thinks, but the supported values depend on the model.[1] For some models, setting reasoning effort to anything other than “none” means removing parameters such as temperature and top p.[2] Check the documentation for the specific model and API you’...
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Key takeaways
- OpenAI describes reasoning effort as a control over how much a model thinks, but the supported values depend on the model.[1]
- For some models, setting reasoning effort to anything other than “none” means removing parameters such as temperature and top p.[2]
- Check the documentation for the specific model and API you’re using rather than assuming settings transfer across models.
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Reasoning effort may look like a simple dial in an API request, but OpenAI’s documentation makes one point clear: its behavior and supported values depend on the model.
The API guide describes reasoning.effort as a setting that guides how much a model thinks while working on a task. The available options can vary; the guide lists values including none, minimal, low, medium, high, xhigh and max as possibilities, not a universal menu for every model.1
Some settings can’t be used together
A model-specific migration guide gives a practical example: when reasoning effort is set to anything other than none, developers should remove temperature, top_p and top_logprobs from the request. It also notes a separate restriction for Chat Completions.2
That’s a compatibility rule, not evidence that every model handles these parameters in the same way. The safe approach is to check the documentation for the particular model and endpoint before carrying settings over from another configuration.
What developers should take away
Treat reasoning effort as its own model-dependent control—not as a setting with identical options everywhere. If a request combines it with other generation parameters, verify that the combination is supported. For example, OpenAI’s GPT-5.2 and GPT-5.4 guide says both models support none as their lowest reasoning-effort setting and identifies it as the default for those models.3
In short: check the model’s supported values and parameter rules first. A setting that works for one model may not be valid for another.
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What is the short answer to "Reasoning Effort Isn’t a Universal Setting"?
OpenAI describes reasoning effort as a control over how much a model thinks, but the supported values depend on the model.[1]
What are the key points to validate first?
OpenAI describes reasoning effort as a control over how much a model thinks, but the supported values depend on the model.[1] For some models, setting reasoning effort to anything other than “none” means removing parameters such as temperature and top p.[2]
What should I do next in practice?
Check the documentation for the specific model and API you’re using rather than assuming settings transfer across models.