Reasoning models | OpenAI API

Reasoning models like GPT-5 are LLMs trained with reinforcement learning to perform reasoning. Reasoning models think before they answer, producing a long internal chain of thought before responding to the user. Reasoning models excel in complex problem solving, coding, scientific reasoning, and multi-step planning for agentic workflows. They're also the best models for Codex CLI, our lightweight coding agent.

We provide smaller, faster models (gpt-5-mini and gpt-5-nano) that are less expensive per token. The larger model (gpt-5) is slower and more expensive but often generates better responses for complex tasks and broad domains.

Get started with reasoning

Call the Responses API and specify your reasoning model and reasoning effort:

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import OpenAI from "openai";

const openai = new OpenAI();

const prompt = `
Write a bash script that takes a matrix represented as a string with 
format '[1,2],[3,4],[5,6]' and prints the transpose in the same format.
`;

const response = await openai.responses.create({
    model: "gpt-5",
    reasoning: { effort: "medium" },
    input: [
        {
            role: "user",
            content: prompt,
        },
    ],
});

console.log(response.output_text);

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from openai import OpenAI

client = OpenAI()

prompt = """
Write a bash script that takes a matrix represented as a string with 
format '[1,2],[3,4],[5,6]' and prints the transpose in the same format.
"""

response = client.responses.create(
    model="gpt-5",
    reasoning={"effort": "medium"},
    input=[
        {
            "role": "user", 
            "content": prompt
        }
    ]
)

print(response.output_text)

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curl https://api.openai.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-5",
    "reasoning": {"effort": "medium"},
    "input": [
      {
        "role": "user",
        "content": "Write a bash script that takes a matrix represented as a string with format \"[1,2],[3,4],[5,6]\" and prints the transpose in the same format."
      }
    ]
  }'

In the example above, the reasoning.effort parameter guides the model on how many reasoning tokens to generate before creating a response to the prompt.

Specify low, medium, or high for this parameter, where low favors speed and economical token usage, and high favors more complete reasoning. The default value is medium, which is a balance between speed and reasoning accuracy.

How reasoning works

Reasoning models introduce reasoning tokens in addition to input and output tokens. The models use these reasoning tokens to "think," breaking down the prompt and considering multiple approaches to generating a response. After generating reasoning tokens, the model produces an answer as visible completion tokens and discards the reasoning tokens from its context.

Here is an example of a multi-step conversation between a user and an assistant. Input and output tokens from each step are carried over, while reasoning tokens are discarded.

Managing the context window

It's important to ensure there's enough space in the context window for reasoning tokens when creating responses. Depending on the problem's complexity, the models may generate anywhere from a few hundred to tens of thousands of reasoning tokens. The exact number of reasoning tokens used is visible in the usage object of the response object, under output_tokens_details:

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{
    "usage": {
        "input_tokens": 75,
        "input_tokens_details": {
            "cached_tokens": 0
        },
        "output_tokens": 1186,
        "output_tokens_details": {
            "reasoning_tokens": 1024
        },
        "total_tokens": 1261
    }
}

Context window lengths are found on the model reference page, and will differ across model snapshots.

Controlling costs

To manage costs with reasoning models, you can limit the total number of tokens the model generates (including both reasoning and final output tokens) by using the max_output_tokens parameter.

Allocating space for reasoning

If the generated tokens reach the context window limit or the max_output_tokens value you've set, you'll receive a response with a status of incomplete and incomplete_details with reason set to max_output_tokens. This might occur before any visible output tokens are produced, meaning you could incur costs for input and reasoning tokens without receiving a visible response.

To prevent this, ensure there's sufficient space in the context window or adjust the max_output_tokens value to a higher number. OpenAI recommends reserving at least 25,000 tokens for reasoning and outputs when you start experimenting with these models. As you become familiar with the number of reasoning tokens your prompts require, you can adjust this buffer accordingly.

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import OpenAI from "openai";

const openai = new OpenAI();

const prompt = `
Write a bash script that takes a matrix represented as a string with 
format '[1,2],[3,4],[5,6]' and prints the transpose in the same format.
`;

const response = await openai.responses.create({
    model: "gpt-5",
    reasoning: { effort: "medium" },
    input: [
        {
            role: "user",
            content: prompt,
        },
    ],
    max_output_tokens: 300,
});

if (
    response.status === "incomplete" &&
    response.incomplete_details.reason === "max_output_tokens"
) {
    console.log("Ran out of tokens");
    if (response.output_text?.length > 0) {
        console.log("Partial output:", response.output_text);
    } else {
        console.log("Ran out of tokens during reasoning");
    }
}

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from openai import OpenAI

client = OpenAI()

prompt = """
Write a bash script that takes a matrix represented as a string with 
format '[1,2],[3,4],[5,6]' and prints the transpose in the same format.
"""

response = client.responses.create(
    model="gpt-5",
    reasoning={"effort": "medium"},
    input=[
        {
            "role": "user", 
            "content": prompt
        }
    ],
    max_output_tokens=300,
)

if response.status == "incomplete" and response.incomplete_details.reason == "max_output_tokens":
    print("Ran out of tokens")
    if response.output_text:
        print("Partial output:", response.output_text)
    else: 
        print("Ran out of tokens during reasoning")

Keeping reasoning items in context

When doing function calling with a reasoning model in the Responses API, we highly recommend you pass back any reasoning items returned with the last function call (in addition to the output of your function). If the model calls multiple functions consecutively, you should pass back all reasoning items, function call items, and function call output items, since the last user message. This allows the model to continue its reasoning process to produce better results in the most token-efficient manner.

The simplest way to do this is to pass in all reasoning items from a previous response into the next one. Our systems will smartly ignore any reasoning items that aren't relevant to your functions, and only retain those in context that are relevant. You can pass reasoning items from previous responses either using the previous_response_id parameter, or by manually passing in all the output items from a past response into the input of a new one.

For advanced use cases where you might be truncating and optimizing parts of the context window before passing them on to the next response, just ensure all items between the last user message and your function call output are passed into the next response untouched. This will ensure that the model has all the context it needs.

Check out this guide to learn more about manual context management.

Encrypted reasoning items

When using the Responses API in a stateless mode (either with store set to false, or when an organization is enrolled in zero data retention), you must still retain reasoning items across conversation turns using the techniques described above. But in order to have reasoning items that can be sent with subsequent API requests, each of your API requests must have reasoning.encrypted_content in the include parameter of API requests, like so:

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curl https://api.openai.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "o4-mini",
    "reasoning": {"effort": "medium"},
    "input": "What is the weather like today?",
    "tools": [ ... function config here ... ],
    "include": [ "reasoning.encrypted_content" ]
  }'

Any reasoning items in the output array will now have an encrypted_content property, which will contain encrypted reasoning tokens that can be passed along with future conversation turns.

Reasoning summaries

While we don't expose the raw reasoning tokens emitted by the model, you can view a summary of the model's reasoning using the the summary parameter. See our model documentation to check which reasoning models support summaries.

Different models support different reasoning summary settings. For example, our computer use model supports the concise summarizer, while o4-mini supports detailed. To access the most detailed summarizer available for a model, set the value of this parameter to auto. auto will be equivalent to detailed for most reasoning models today, but there may be more granular settings in the future.

Reasoning summary output is part of the summary array in the reasoning output item. This output will not be included unless you explicitly opt in to including reasoning summaries.

The example below shows how to make an API request that includes a reasoning summary.

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import OpenAI from "openai";
const openai = new OpenAI();

const response = await openai.responses.create({
  model: "gpt-5",
  input: "What is the capital of France?",
  reasoning: {
    effort: "low",
    summary: "auto",
  },
});

console.log(response.output);

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from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-5",
    input="What is the capital of France?",
    reasoning={
        "effort": "low",
        "summary": "auto"
    }
)

print(response.output)

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curl https://api.openai.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-5",
    "input": "What is the capital of France?",
    "reasoning": {
        "effort": "low",
        "summary": "auto"
    }
  }'

This API request will return an output array with both an assistant message and a summary of the model's reasoning in generating that response.

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[
    {
        "id": "rs_6876cf02e0bc8192b74af0fb64b715ff06fa2fcced15a5ac",
        "type": "reasoning",
        "summary": [
            {
                "type": "summary_text",
                "text": "**Answering a simple question**\n\nI\u2019m looking at a straightforward question: the capital of France is Paris. It\u2019s a well-known fact, and I want to keep it brief and to the point. Paris is known for its history, art, and culture, so it might be nice to add just a hint of that charm. But mostly, I\u2019ll aim to focus on delivering a clear and direct answer, ensuring the user gets what they\u2019re looking for without any extra fluff."
            }
        ]
    },
    {
        "id": "msg_6876cf054f58819284ecc1058131305506fa2fcced15a5ac",
        "type": "message",
        "status": "completed",
        "content": [
            {
                "type": "output_text",
                "annotations": [],
                "logprobs": [],
                "text": "The capital of France is Paris."
            }
        ],
        "role": "assistant"
    }
]

Advice on prompting

There are some differences to consider when prompting a reasoning model. Reasoning models provide better results on tasks with only high-level guidance, while GPT models often benefit from very precise instructions.

• A reasoning model is like a senior co-worker—you can give them a goal to achieve and trust them to work out the details.
• A GPT model is like a junior coworker—they'll perform best with explicit instructions to create a specific output.

For more information on best practices when using reasoning models, refer to this guide.

Prompt examples

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import OpenAI from "openai";

const openai = new OpenAI();

const prompt = `
Instructions:
- Given the React component below, change it so that nonfiction books have red
  text. 
- Return only the code in your reply
- Do not include any additional formatting, such as markdown code blocks
- For formatting, use four space tabs, and do not allow any lines of code to 
  exceed 80 columns

const books = [
  { title: 'Dune', category: 'fiction', id: 1 },
  { title: 'Frankenstein', category: 'fiction', id: 2 },
  { title: 'Moneyball', category: 'nonfiction', id: 3 },
];

export default function BookList() {
  const listItems = books.map(book =>
    <li>
      {book.title}
    </li>
  );

  return (
    <ul>{listItems}</ul>
  );
}
`.trim();

const response = await openai.responses.create({
    model: "gpt-5",
    input: [
        {
            role: "user",
            content: prompt,
        },
    ],
});

console.log(response.output_text);

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from openai import OpenAI

client = OpenAI()

prompt = """
Instructions:
- Given the React component below, change it so that nonfiction books have red
  text. 
- Return only the code in your reply
- Do not include any additional formatting, such as markdown code blocks
- For formatting, use four space tabs, and do not allow any lines of code to 
  exceed 80 columns

const books = [
  { title: 'Dune', category: 'fiction', id: 1 },
  { title: 'Frankenstein', category: 'fiction', id: 2 },
  { title: 'Moneyball', category: 'nonfiction', id: 3 },
];

export default function BookList() {
  const listItems = books.map(book =>
    <li>
      {book.title}
    </li>
  );

  return (
    <ul>{listItems}</ul>
  );
}
"""

response = client.responses.create(
    model="gpt-5",
    input=[
        {
            "role": "user",
            "content": prompt,
        }
    ]
)

print(response.output_text)

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import OpenAI from "openai";

const openai = new OpenAI();

const prompt = `
I want to build a Python app that takes user questions and looks 
them up in a database where they are mapped to answers. If there 
is close match, it retrieves the matched answer. If there isn't, 
it asks the user to provide an answer and stores the 
question/answer pair in the database. Make a plan for the directory 
structure you'll need, then return each file in full. Only supply 
your reasoning at the beginning and end, not throughout the code.
`.trim();

const response = await openai.responses.create({
    model: "gpt-5",
    input: [
        {
            role: "user",
            content: prompt,
        },
    ],
});

console.log(response.output_text);

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from openai import OpenAI

client = OpenAI()

prompt = """
I want to build a Python app that takes user questions and looks 
them up in a database where they are mapped to answers. If there 
is close match, it retrieves the matched answer. If there isn't, 
it asks the user to provide an answer and stores the 
question/answer pair in the database. Make a plan for the directory 
structure you'll need, then return each file in full. Only supply 
your reasoning at the beginning and end, not throughout the code.
"""

response = client.responses.create(
    model="gpt-5",
    input=[
        {
            "role": "user",
            "content": prompt,
        }
    ]
)

print(response.output_text)

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import OpenAI from "openai";

const openai = new OpenAI();

const prompt = `
What are three compounds we should consider investigating to 
advance research into new antibiotics? Why should we consider 
them?
`;

const response = await openai.responses.create({
    model: "gpt-5",
    input: [
        {
            role: "user",
            content: prompt,
        },
    ],
});

console.log(response.output_text);

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from openai import OpenAI

client = OpenAI()

prompt = """
What are three compounds we should consider investigating to 
advance research into new antibiotics? Why should we consider 
them?
"""

response = client.responses.create(
    model="gpt-5",
    input=[
        {
            "role": "user", 
            "content": prompt
        }
    ]
)

print(response.output_text)

Use case examples

Some examples of using reasoning models for real-world use cases can be found in the cookbook.