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

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@SinhSinhAn 已经在做这个了。

开始于 2026年4月25日。

  • #3124 来自 @SinhSinhAn —— 未关闭

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Issue 类型
功能
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基本清楚
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技术栈
python
领域
api

调研方向

该 issue 没有指明实现文件、测试或入口点。首先定位 Python 库中与模型相关的公共 API 和现有模型元数据，然后确定应如何表示能力检查和有效的 reasoning-effort 值；当已记录所列模型系列的行为，并覆盖受支持和不受支持的参数时，即表示完成。

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描述

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

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