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Create Chat Completion

Creates a model response for the given chat conversation. 
client.chat.completions.create(
    model="gpt-5.2",
    messages=[
        {"role": "developer", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is recursion?"}
    ]
)

​
Parameters

​
messages
array
required
A list of messages comprising the conversation so far. Depending on the model you use, different message types (modalities) are supported, like text, images, and audio.See Message Format below for details.
​
model
string
required
Model ID used to generate the response, like gpt-4o or o3. OpenAI offers a wide range of models with different capabilities, performance characteristics, and price points. Refer to the model guide to browse and compare available models.
​
temperature
float
default:"1.0"
What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both.
​
max_completion_tokens
integer
An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.
​
max_tokens
integer
Deprecated in favor of max_completion_tokens. The maximum number of tokens that can be generated in the chat completion. This value is not compatible with o-series models.
​
n
integer
default:"1"
How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs.
​
stream
boolean
default:"false"
If set to true, the model response data will be streamed to the client as it is generated using server-sent events. See Streaming for more information.
​
stop
string | array
Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence. Not supported with latest reasoning models o3 and o4-mini.
​
presence_penalty
float
default:"0"
Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model’s likelihood to talk about new topics.
​
frequency_penalty
float
default:"0"
Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model’s likelihood to repeat the same line verbatim.
​
logit_bias
object
Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100.
​
logprobs
boolean
default:"false"
Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message.
​
top_logprobs
integer
An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to true if this parameter is used.
​
top_p
float
default:"1.0"
An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.We generally recommend altering this or temperature but not both.
​
seed
integer
This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed.
​
tools
array
A list of tools the model may call. You can provide either custom tools or function tools. See Function Calling below.
​
tool_choice
string | object
Controls which (if any) tool is called by the model. none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools.Specifying a particular tool via {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool.none is the default when no tools are present. auto is the default if tools are present.
​
parallel_tool_calls
boolean
default:"true"
Whether to enable parallel function calling during tool use.
​
response_format
object
An object specifying the format that the model must output.Setting to {"type": "json_schema", "json_schema": {...}} enables Structured Outputs which ensures the model will match your supplied JSON schema.Setting to {"type": "json_object"} enables the older JSON mode, which ensures the message the model generates is valid JSON.
​
reasoning_effort
string
Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh.
  • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high.
  • All models before gpt-5.1 default to medium reasoning effort.
  • The gpt-5-pro model defaults to (and only supports) high reasoning effort.
  • xhigh is supported for all models after gpt-5.1-codex-max.
​
user
string
Deprecated - being replaced by safety_identifier and prompt_cache_key. A stable identifier for your end-users.
​
metadata
object
Set of 16 key-value pairs that can be attached to an object. Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
​
store
boolean
Whether or not to store the output of this chat completion request for use in model distillation or evals products. Supports text and image inputs. Note: image inputs over 8MB will be dropped.

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Response

Returns a ChatCompletion object. 
{
    "id": "chatcmpl-123",
    "object": "chat.completion",
    "created": 1677652288,
    "model": "gpt-5.2",
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "Recursion is when a function calls itself..."
            },
            "finish_reason": "stop"
        }
    ],
    "usage": {
        "prompt_tokens": 20,
        "completion_tokens": 50,
        "total_tokens": 70
    }
}
​
id
string
A unique identifier for the chat completion.
​
object
string
The object type, which is always chat.completion.
​
created
integer
The Unix timestamp (in seconds) of when the chat completion was created.
​
model
string
The model used for the chat completion.
​
choices
array
A list of chat completion choices. Can be more than one if n is greater than 1.
​
usage
object
Usage statistics for the completion request.
​
system_fingerprint
string
This fingerprint represents the backend configuration that the model runs with. Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.

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Message Format

Messages use different roles to distinguish between participants in the conversation:

​
Developer Messages

High-level instructions for the model. Preferred for o1 models and newer. 
{"role": "developer", "content": "You are a helpful assistant."}

​
System Messages

Developer-provided instructions that the model should follow. With o1 models and newer, use developer messages instead. 
{"role": "system", "content": "You are a helpful assistant."}

​
User Messages

Messages sent by an end user, containing prompts or additional context information. 
{"role": "user", "content": "What is the capital of France?"}

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Assistant Messages

Messages sent by the model in response to user messages. 
{"role": "assistant", "content": "The capital of France is Paris."}

​
Tool Messages

Messages containing the result of a tool call. 
{
    "role": "tool",
    "content": '{"temperature": 72}',
    "tool_call_id": "call_abc123"
}

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Multi-modal Content

User messages can include images and other content types: 
{
    "role": "user",
    "content": [
        {"type": "text", "text": "What's in this image?"},
        {
            "type": "image_url",
            "image_url": {"url": "https://example.com/image.jpg"}
        }
    ]
}

​
Function Calling

You can provide tools for the model to call during the completion.

​
Basic Example

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather in a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"]
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather in Boston?"}],
    tools=tools,
    tool_choice="auto"
)

​
Handling Tool Calls

from openai import OpenAI
import json

client = OpenAI()

def get_weather(location, unit="fahrenheit"):
    # Your implementation here
    return {"temperature": 72, "unit": unit, "condition": "sunny"}

messages = [{"role": "user", "content": "What's the weather in Boston?"}]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    tools=tools
)

message = response.choices[0].message

# Check if the model wants to call a function
if message.tool_calls:
    # Extend conversation with assistant's reply
    messages.append(message)
    
    # Execute each tool call
    for tool_call in message.tool_calls:
        function_name = tool_call.function.name
        function_args = json.loads(tool_call.function.arguments)
        
        # Call the function
        if function_name == "get_weather":
            function_response = get_weather(
                location=function_args.get("location"),
                unit=function_args.get("unit", "fahrenheit")
            )
        
        # Add function response to messages
        messages.append(
            {
                "role": "tool",
                "content": json.dumps(function_response),
                "tool_call_id": tool_call.id
            }
        )
    
    # Get final response from the model
    final_response = client.chat.completions.create(
        model="gpt-4o",
        messages=messages
    )
    
    print(final_response.choices[0].message.content)

​
Parallel Function Calling

By default, the model can call multiple functions in parallel: 
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather in Boston and New York?"}],
    tools=tools,
    parallel_tool_calls=True  # Default behavior
)

# The response may contain multiple tool_calls
for tool_call in response.choices[0].message.tool_calls:
    print(f"Calling {tool_call.function.name}")

​
Forcing Tool Usage

# Force the model to call a specific function
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather?"}],
    tools=tools,
    tool_choice={"type": "function", "function": {"name": "get_weather"}}
)

# Require the model to call at least one tool
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather?"}],
    tools=tools,
    tool_choice="required"
)

# Disable tool calling
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather?"}],
    tools=tools,
    tool_choice="none"
)

​
Structured Outputs with Pydantic

Use the .parse() method to automatically parse responses into Pydantic models: 
from pydantic import BaseModel
from openai import OpenAI

client = OpenAI()

class Step(BaseModel):
    explanation: str
    output: str

class MathResponse(BaseModel):
    steps: list[Step]
    final_answer: str

completion = client.chat.completions.parse(
    model="gpt-4o-2024-08-06",
    messages=[
        {"role": "system", "content": "You are a helpful math tutor."},
        {"role": "user", "content": "solve 8x + 31 = 2"},
    ],
    response_format=MathResponse,
)

message = completion.choices[0].message
if message.parsed:
    print(message.parsed.steps)
    print("answer:", message.parsed.final_answer)

​
Examples

​
Basic Chat Completion

from openai import OpenAI

client = OpenAI()

completion = client.chat.completions.create(
    model="gpt-5.2",
    messages=[
        {"role": "developer", "content": "Talk like a pirate."},
        {"role": "user", "content": "How do I check if a Python object is an instance of a class?"}
    ]
)

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

​
Multi-turn Conversation

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who won the world series in 2020?"},
]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages
)

# Add assistant's response to conversation
messages.append(response.choices[0].message)

# Continue the conversation
messages.append({"role": "user", "content": "Where was it played?"})

response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages
)

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

​
Vision with Images

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
                    }
                }
            ]
        }
    ]
)

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

​
JSON Mode

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant designed to output JSON."},
        {"role": "user", "content": "List 3 colors in JSON format"}
    ],
    response_format={"type": "json_object"}
)

print(response.choices[0].message.content)
# Output: {"colors": ["red", "blue", "green"]}

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