Creates a chat completion from the supplied request.

An opaque, application-owned conversation identifier. The value is not returned in the response and must not contain secrets or personally identifiable information.

The request body.

Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard. Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

This field is being replaced by safety_identifier and prompt_cache_key. Use prompt_cache_key instead to maintain caching optimizations. A stable identifier for your end-users. Used to boost cache hit rates by better bucketing similar requests and to help OpenAI detect and prevent abuse. Learn more.

Options for prompt caching. Supported for gpt-5.6 and later models. By default, OpenAI automatically chooses one implicit cache breakpoint. You can add explicit breakpoints to content blocks with prompt_cache_breakpoint. Each request can write up to four breakpoints. For cache matching, OpenAI considers up to the latest 80 breakpoints in the conversation, without a content-block lookback limit. Set mode to explicit to disable the implicit breakpoint. The ttl defaults to 30m, which is currently the only supported value. See the prompt caching guide for current details.

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.

The model deployment to use for this chat completion.

Specifies the processing type used for serving the request. - If set to 'auto', then the request will be processed with the service tier configured in the Project settings. Unless otherwise configured, the Project will use 'default'. - If set to 'default', then the request will be processed with the standard pricing and performance for the selected model. - If set to 'flex', then the request will be processed with the Flex Processing service tier. - To opt-in to Fast mode at the request level, include the service_tier=fast or service_tier=priority parameter for Responses or Chat Completions. The response will show service_tier=priority regardless of if you specify service_tier=fast or priority in your request. - When not set, the default behavior is 'auto'. When the service_tier parameter is set, the response body will include the service_tier value based on the processing mode actually used to serve the request. This response value may be different from the value set in the parameter.

Output types that you would like the model to generate. Most models are capable of generating text, which is the default: ["text"] The gpt-4o-audio-preview model can also be used to generate audio. To request that this model generate both text and audio responses, you can use: ["text", "audio"]

Constrains the verbosity of the model's response. Lower values will result in more concise responses, while higher values will result in more verbose responses. Currently supported values are low, medium, and high. The default is medium.

Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, xhigh, and max. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response. Not all reasoning models support every value. See the reasoning guide for model-specific support.

An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.

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.

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.

Approximate location parameters for the search.

The user's two-letter ISO 3166-1 alpha-2 code, for example, US.

Free text input for the region of the user, e.g. California.

Free text input for the city of the user, e.g. San Francisco.

The IANA timezone of the user, e.g. America/Los_Angeles.

High level guidance for the amount of context window space to use for the search. One of low, medium, or high. medium is the default.

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. Learn more in the Structured Outputs guide. Setting to { "type": "json_object" } enables the older JSON mode, which ensures the message the model generates is valid JSON. Using json_schema is preferred for models that support it.

Default response format. Used to generate text responses.

The type of response format being defined. Always text.

JSON object response format. An older method of generating JSON responses. Using json_schema is recommended for models that support it. Note that the model will not generate JSON without a system or user message instructing it to do so.

The type of response format being defined. Always json_object.

JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.

The type of response format being defined. Always json_schema.

The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.

Parameters for audio output. Required when audio output is requested with modalities: ["audio"]. Learn more.

A built-in voice name or a custom voice reference.

Whether or not to store the output of this chat completion request for use in our model distillation or evals products. Supports text and image inputs. Note: image inputs over 8MB will be dropped.

Configuration for running moderation on the input and output of this response.

The moderation model to use for moderated completions, e.g. 'omni-moderation-latest'.

The policy to apply to moderated response input and output.

The moderation policy for the response input.

The moderation policy for the response input.

If set to true, the model response data will be streamed to the client as it is generated using server-sent events. See the Streaming section below for more information, along with the streaming responses guide for more information on how to handle the streaming events.

Not supported with latest reasoning models o3 and o4-mini. Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

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. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.

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.

The maximum number of tokens that can be generated in the chat completion. This value can be used to control costs for text generated via API. This value is now deprecated in favor of max_completion_tokens, and is not compatible with o-series models.

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.

Configuration for a Predicted Output, which can greatly improve response times when large parts of the model response are known ahead of time. This is most common when you are regenerating a file with only minor changes to most of the content.

The type of the predicted content you want to provide. This type is currently always content.

The content that should be matched when generating a model response. If generated tokens would match this content, the entire model response can be returned much more quickly.

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, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.

Options for streaming response. Only set this when you set stream: true.

If set, an additional chunk will be streamed before the data: [DONE] message. The usage field on this chunk shows the token usage statistics for the entire request, and the choices field will always be an empty array. All other chunks will also include a usage field, but with a null value. NOTE: If the stream is interrupted, you may not receive the final usage chunk which contains the total token usage for the request.

When true, stream obfuscation will be enabled. Stream obfuscation adds random characters to an obfuscation field on streaming delta events to normalize payload sizes as a mitigation to certain side-channel attacks. These obfuscation fields are included by default, but add a small amount of overhead to the data stream. You can set include_obfuscation to false to optimize for bandwidth if you trust the network links between your application and the OpenAI API.

A list of tools the model may call. You can provide either custom tools or function tools.

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.

Allowed tool configuration type. Always allowed_tools.

Constrains the tools available to the model to a pre-defined set.

Constrains the tools available to the model to a pre-defined set. auto allows the model to pick from among the allowed tools and generate a message. required requires the model to call one or more of the allowed tools.

A list of tool definitions that the model should be allowed to call. For the Chat Completions API, the list of tool definitions might look like: json [ { "type": "function", "function": { "name": "get_weather" } }, { "type": "function", "function": { "name": "get_time" } } ]

For function calling, the type is always function.

For custom tool calling, the type is always custom.

Whether to enable parallel function calling during tool use.

Deprecated in favor of tool_choice. Controls which (if any) function is called by the model. none means the model will not call a function and instead generates a message. auto means the model can pick between generating a message or calling a function. Specifying a particular function via {"name": "my_function"} forces the model to call that function. none is the default when no functions are present. auto is the default if functions are present.

The name of the function to call.

Deprecated in favor of tools. A list of functions the model may generate JSON inputs for.

User security context contains several parameters that describe the application itself, and the end user that interacts with the application. These fields assist your security operations teams to investigate and mitigate security incidents by providing a comprehensive approach to protecting your AI applications. Learn more about protecting AI applications using Microsoft Defender for Cloud.

The name of the application. Sensitive personal information should not be included in this field.

This identifier is the Microsoft Entra ID (formerly Azure Active Directory) user object ID used to authenticate end-users within the generative AI application. Sensitive personal information should not be included in this field.

The Microsoft 365 tenant ID the end user belongs to. It's required when the generative AI application is multitenant.

Captures the original client's IP address.

Per-request configuration for Model Router inference calls. Supported on Model Router endpoints (deployed or instant if supported). Returns a 400 (Bad Request) error when the target is not a Model Router endpoint.

Request settings that control Model Router session affinity.

Request modes supported by Model Router session affinity.

An opaque, application-owned conversation identifier. Use the same value for related requests. The value is not returned in the response and must not contain secrets or personally identifiable information.

A unique identifier for the chat completion.

A list of chat completion choices. Can be more than one if n is greater than 1.

The Unix timestamp (in seconds) of when the chat completion was created.

The model deployment used for the creation of this chat completion.

Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard. Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

Specifies the processing type used for serving the request. - If set to 'auto', then the request will be processed with the service tier configured in the Project settings. Unless otherwise configured, the Project will use 'default'. - If set to 'default', then the request will be processed with the standard pricing and performance for the selected model. - If set to 'flex', then the request will be processed with the Flex Processing service tier. - To opt-in to Fast mode at the request level, include the service_tier=fast or service_tier=priority parameter for Responses or Chat Completions. The response will show service_tier=priority regardless of if you specify service_tier=fast or priority in your request. - When not set, the default behavior is 'auto'. When the service_tier parameter is set, the response body will include the service_tier value based on the processing mode actually used to serve the request. This response value may be different from the value set in the parameter.

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.

The object type, which is always chat.completion.

Usage statistics for the completion request.

Moderation results or errors for the request input and generated output.

Moderation results or an error for a chat completion moderation check.

Successful moderation results for the request input or generated output.

The object type, which is always moderation_results.

The moderation model used to generate the results.

A list of moderation results.

An error produced while attempting moderation.

The object type, which is always error.

The error code.

The error message.

Moderation results or an error for a chat completion moderation check.

Successful moderation results for the request input or generated output.

The object type, which is always moderation_results.

The moderation model used to generate the results.

A list of moderation results.

An error produced while attempting moderation.

The object type, which is always error.

The error code.

The error message.

Model selection metadata providing visibility into routing decisions.

Routing decision metadata from Model Router.

The routing mode used by Model Router for the request.

Effective Model Router session affinity metadata for a request.

Effective modes reported for Model Router session affinity.

Conversation identifier sources supported by Model Router session affinity.

Final outcomes reported for Model Router session affinity.

Ordered trace of routing decisions. Contains one entry for Chat Completions, and may contain multiple entries for Responses API or agent scenarios where multiple inference turns occur.

The public explanation for this block.

The public continuation instruction.

The public explanation for this block.

The public continuation instruction.