Chat Completions

Request

logprobs · bool

Whether to return log probabilities of the output tokens or not.

Default: False

max_completion_tokens · integer | null

The maximum number of tokens that can be generated in the completion, including reasoning tokens. The total length of input tokens and generated tokens is limited by the model’s context length.

Default settings: qwen-3-32b = 40k | llama-3.3-70b = 64k.

messages · object[] · required

A list of messages comprising the conversation so far.

Note: System prompts must be passed to the messages parameter as a string. Support for other object types will be added in future releases.

model · string · required

Available options:

• llama3.1-8b
• llama-3.3-70b
• qwen-3-32b
• qwen-3-235b-a22b-instruct-2507 (preview)
• gpt-oss-120b
• zai-glm-4.6 (preview)

parallel_tool_calls · boolean | null

Whether to enable parallel function calling during tool use. When enabled (default), the model can request multiple tool calls simultaneously in a single response. When disabled, the model will only request one tool call at a time.

Default: true

prediction · object | null

Configuration for a Predicted Output, which can greatly speed up response times when large parts of the model response are known in advance. This is most common when you are regenerating a file with mostly minor changes to the content.

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Static Content

Static predicted output content, such as the content of a text or code file that is being regenerated.

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content · string | array · required

The content that should be matched when generating a model response. If continuous token sequences from the generated tokens match this content, the entire model response can be returned faster.

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Text content string

The content used for a given Predicted Output. Typically the text of a file you are regenerating with only minor changes.

Array of content parts array

An array of content parts with a defined type. Supported options may differ based on the model that is used to generate the response. May contain text inputs.

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text · string · required

The text content.

type · string · required

The type of content part. Always text.

type · string · required

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

Visit our page on Predicted Outputs for more information and examples.

reasoning_effort · string | null

Controls the amount of reasoning the model performs. Available values:

• "low" - Minimal reasoning, faster responses
• "medium" - Moderate reasoning (default)
• "high" - Extensive reasoning, more thorough analysis

This flag is only available for gpt-oss-120b model.

response_format · object | null

An object that controls the format of the model response.

Setting to { "type": "json_schema", "json_schema": { "name": "schema_name", "strict": true, "schema": {...} } } enforces schema compliance by ensuring that the model output conforms to your specified JSON schema. See Structured Outputs for more information.

Setting { "type": "json_object" } enables the legacy JSON mode, ensuring that the model output is valid JSON. However, using json_schema is recommended for models that support it.

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Text object

Default response format. Generates plain text responses.

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type · string · required

The type of response format being defined. Always text.

JSON schema object

Generates structured JSON output that conforms to the specified schema. Use this format when you need the model to return structured JSON.

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json_schema · object · required

Structured Outputs configuration options.

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name · string · required

An optional name for your schema.

description · string

A description of the response format’s purpose, used by the model to determine how to generate its response in that format.

schema · object

A valid JSON Schema object that defines the structure, types, and requirements for the response. Supports standard JSON Schema features including types (string, number, boolean, integer, object, array, enum, anyOf, null), nested structures, required fields, and additionalProperties (must be set to false).

strict · boolean

When set to true, enforces strict adherence to the schema. The model will only return fields defined in the schema and with the correct types. When false, behaves similar to JSON mode but uses the schema as a guide. Defaults to false.

type · string · required

The type of response format being defined. Always json_schema.

JSON object object

A legacy method for generating JSON responses. Using json_schema is recommended for models that support it. To use json_object remember to also include a system or user message to specify the desired format.

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type · string · required

The type of response format being defined. Always json_object.

When using JSON object, you must explicitly instruct the model to generate JSON through a system or user message. json_object is not compatible with streaming - stream must be set to false.

seed · integer | null

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.

stop · string | null

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

stream · boolean | null

If set, partial message deltas will be sent.

temperature · number | null

What sampling temperature to use, between 0 and 1.5. 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.

top_logprobs · integer | null

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 · number | null

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.

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.

tools · object | null

A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for.

Specifying tools consumes prompt tokens in the context. If too many are given, the model may perform poorly or you may hit context length limitations

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tools.function.description · string

A description of what the function does, used by the model to choose when and how to call the function.

tools.function.name · string

The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

tools.function.parameters · object

The parameters the functions accepts, described as a JSON Schema object. Omitting parameters defines a function with an empty parameter list.

tools.type · string

The type of the tool. Currently, only function is supported.

user · string | null

A unique identifier representing your end-user, which can help to monitor and detect abuse.

Response

id · string

A unique identifier for the chat completion.

choices · object[]

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

Show choice properties

finish_reason · string

The reason the model stopped generating tokens. Possible values: stop, length, content_filter, tool_calls.

index · integer

The index of the choice in the list of choices.

message · object

A chat completion message generated by the model.

Show message properties

content · string

The contents of the message.

role · string

The role of the author of this message (always assistant).

reasoning · string

The model’s reasoning content when using reasoning models (e.g., gpt-oss-120b with reasoning_effort set).

created · integer

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

model · string

The model used for the chat completion.

object · string

The object type, which is always chat.completion.

usage · object

Usage statistics for the completion request.

Show usage properties

prompt_tokens · integer

Number of tokens in the prompt.

completion_tokens · integer

Number of tokens in the generated completion.

total_tokens · integer

Total number of tokens used in the request (prompt + completion).

completion_tokens_details · object

Breakdown of completion tokens when using Predicted Outputs.

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accepted_prediction_tokens · integer

When using Predicted Outputs, the number of tokens in the prediction that appeared in the completion.

rejected_prediction_tokens · integer

When using Predicted Outputs, the number of tokens in the prediction that did not appear in the completion. Like reasoning tokens, these tokens are still counted in the total completion tokens for the purposes of billing, output, and context window limits.

Python

from cerebras.cloud.sdk import Cerebras
import os 

client = Cerebras(api_key=os.environ.get("CEREBRAS_API_KEY"),)

chat_completion = client.chat.completions.create(
    model="gpt-oss-120b",
    messages=[
        {"role": "user", "content": "Hello!",}
    ],
)
print(chat_completion)

Response

{
  "id": "chatcmpl-292e278f-514e-4186-9010-91ce6a14168b",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "content": "Hello! How can I assist you today?",
        "reasoning": "The user is asking for a simple greeting to the world. This is a straightforward request that doesn't require complex analysis. I should provide a friendly, direct response.",
        "role": "assistant"
      }
    }
  ],
  "created": 1723733419,
  "model": "gpt-oss-120b",
  "system_fingerprint": "fp_70185065a4",
  "object": "chat.completion",
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 10,
    "total_tokens": 22,
    "completion_tokens_details": {
      "accepted_prediction_tokens": 0,
      "rejected_prediction_tokens": 0
    }
  },
  "time_info": {
    "queue_time": 0.000073161,
    "prompt_time": 0.0010744798888888889,
    "completion_time": 0.005658071111111111,
    "total_time": 0.022224903106689453,
    "created": 1723733419
  }
}