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How to set reasoning effort in OpenAI o1

Q: How to set reasoning effort in OpenAI o1

To set reasoning effort in OpenAI's o1 model, adjust parameters like temperature and max_tokens in the API call to control creativity and output length. Higher temperature increases exploratory reasoning, while lower values focus on precise, deterministic answers.

Quick answer

To set reasoning effort in OpenAI's o1 model, adjust parameters like temperature and max_tokens in the API call to control creativity and output length. Higher temperature increases exploratory reasoning, while lower values focus on precise, deterministic answers.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0

Setup

Install the OpenAI Python SDK and set your API key as an environment variable to authenticate requests.

bash

pip install openai>=1.0

Step by step

Use the temperature parameter to control reasoning effort: higher values (e.g., 0.7-1.0) encourage creative, exploratory reasoning, while lower values (e.g., 0-0.3) produce focused, deterministic answers. Adjust max_tokens to allow longer reasoning chains.

python

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

response = client.chat.completions.create(
    model="o1",
    messages=[{"role": "user", "content": "Explain the reasoning behind Newton's laws."}],
    temperature=0.8,
    max_tokens=512
)

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

output

Newton's laws describe the relationship between a body and the forces acting upon it, explaining motion through inertia, acceleration proportional to force, and action-reaction pairs.

Common variations

Use temperature=0 for precise, factual answers with minimal reasoning exploration.
Set top_p to control nucleus sampling for alternative reasoning paths.
Use max_tokens to limit or extend the reasoning chain length.
Async calls can be made with OpenAI SDK's async methods for concurrency.

python

import asyncio
import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

async def async_reasoning():
    response = await client.chat.completions.acreate(
        model="o1",
        messages=[{"role": "user", "content": "Explain the reasoning behind Newton's laws."}],
        temperature=0.5,
        max_tokens=512
    )
    print(response.choices[0].message.content)

asyncio.run(async_reasoning())

output

Newton's laws describe how forces affect motion, including inertia, acceleration proportional to force, and equal and opposite reactions.

Troubleshooting

If responses are too vague or short, increase max_tokens and temperature.
If answers are inconsistent or hallucinating, lower temperature and verify prompt clarity.
Ensure your API key is correctly set in os.environ["OPENAI_API_KEY"] to avoid authentication errors.

✅

Key Takeaways

Adjust temperature to control the creativity and depth of reasoning in o1 model outputs.
Use max_tokens to allow sufficient length for complex reasoning chains.
Lower temperature values yield more precise, deterministic answers; higher values encourage exploratory reasoning.
Async API calls enable concurrent reasoning tasks with the OpenAI SDK.
Proper environment variable setup is essential to avoid authentication issues.

Verified 2026-04 · o1

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