Reasoning Control

For models that support reasoning (chain-of-thought), you can control the depth of reasoning with the reasoning_effort parameter.

Prerequisites

• An API key (get one here)
• OpenAI SDK installed (Quickstart)
• A reasoning-capable model (see table below)

What you'll learn:

• Which models support reasoning control
• How to use the reasoning_effort parameter
• How to disable reasoning for Qwen3 models

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Supported Models

Model	Reasoning Control
o1, o1-mini, o3, o3-mini, o4-mini	reasoning_effort parameter
Gemini 2.5	reasoning_effort parameter
Claude 3.7, Claude 4	reasoning_effort parameter
Qwen3	/no_think prompt keyword

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Using reasoning_effort

Python

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="claude-sonnet-4",
    messages=[
        {"role": "system", "content": "You are a concise and helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"},
    ],
    reasoning_effort="low",  # "low", "medium", or "high"
)

print(response.choices[0].message.content)

The reasoning_effort parameter accepts three values:

• low — Fast responses, minimal reasoning
• medium — Balanced reasoning depth
• high — Deep reasoning for complex problems

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Disabling Reasoning for Qwen3

For Qwen3 models, prepend /no_think to your prompt to disable reasoning:

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="Qwen3-30B-A3B-FP8",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "/no_think What is your name?"},
    ],
)

print(response.choices[0].message.content)

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Next Steps

• Chat Completions — Standard chat API usage
• Function Calling — Connect reasoning models to external tools
• Streaming — Stream reasoning model responses