Add convenience methods for detecting model parameter support (temperature, reasoning)

Confirm this is a feature request for the Python library and not the underlying OpenAI API.

• This is a feature request for the Python library

Describe the feature or improvement you're requesting

Feature request

When building applications that support multiple OpenAI models (e.g., gpt-4.1-mini, gpt-5, gpt-5.1, gpt-5.4), developers need to know which parameters each model supports. Currently this requires maintaining hardcoded model lists or prefix checks, which break as new models are released.

The problem

Different model families have different parameter constraints:

• temperature: Supported by gpt-4.x but rejected by gpt-5.x reasoning models when reasoning_effort != none
• reasoning: Only supported by gpt-5 family models, not gpt-4.x
• reasoning.effort values: Vary by model — gpt-5 supports minimal/low/medium/high, gpt-5.1+ adds none, gpt-5.4+ adds xhigh

Today, developers must write code like:

def is_reasoning_model(model: str) -> bool:
    return model.startswith("gpt-5")

def get_reasoning_effort_options(model: str) -> list[str]:
    if not is_reasoning_model(model):
        return []
    if model.startswith("gpt-5."):
        minor = int(model[6:].split("-")[0])
        options = ["none", "low", "medium", "high"]
        if minor >= 4:
            options.append("xhigh")
        return options
    return ["minimal", "low", "medium", "high"]

This is fragile — it breaks whenever a new model family is released, and the version-based logic for effort levels is reverse-engineered from documentation rather than provided by the SDK.

Proposed solution

Add model capability metadata to the SDK in some way, for example:

# Check if a parameter is supported
openai.models.supports(model="gpt-5.4", parameter="temperature")  # False
openai.models.supports(model="gpt-5.4", parameter="reasoning")  # True

# Get valid values for an enum parameter
openai.models.valid_values(model="gpt-5.4", parameter="reasoning.effort")
# ["none", "low", "medium", "high", "xhigh"]

openai.models.valid_values(model="gpt-5", parameter="reasoning.effort")
# ["minimal", "low", "medium", "high"]

Or alternatively, expose it as model metadata:

from openai.models import get_model_capabilities

caps = get_model_capabilities("gpt-5.4")
caps.supports_temperature  # False
caps.supports_reasoning  # True
caps.reasoning_effort_options  # ["none", "low", "medium", "high", "xhigh"]

Use case

This came up while migrating azure-search-openai-demo from Chat Completions to the Responses API. The app supports multiple model deployments and needs to dynamically adjust which parameters it sends and presents in the UI for users. I imagine there are many similar apps that provide users with model options and parameters.

Additional context

No response

The issue names no implementation files, tests, or entry points. Start by locating the Python library's model-related public API and existing model metadata, then determine how capability checks and valid reasoning-effort values should be represented; done means documented behavior for the listed model families and coverage for supported and unsupported parameters.