↑↓ Navigateenter Openesc Close
Dashboard
MODEL IDminimax-m3
live

MiniMax M3

MiniMax
by MiniMax

MiniMax M3 is MiniMax's new flagship open-weight model for coding, agentic execution, and multimodal reasoning. It is built around the MiniMax Sparse Attention architecture, supports up to 1 million tokens of context, accepts image and video input in addition to text, and is designed for long-running software, research, browsing, and desktop-operation workflows that need strong tool use and sustained multi-step performance.

MiniMax M3

API Options

Platform-level options for task execution and delivery.

taskType

stringrequiredvalue: textInference

Identifier for the type of task being performed

taskUUID

stringrequiredUUID v4

UUID v4 identifier for tracking tasks and matching async responses. Must be unique per task.

outputFormat

stringdefault: TEXT

Specifies the file format of the generated output. The available values depend on the task type and the specific model's capabilities.

    Allowed values1 value

    webhookURL

    stringURI

    Specifies a webhook URL where JSON responses will be sent via HTTP POST when generation tasks complete. For batch requests with multiple results, each completed item triggers a separate webhook call as it becomes available.

    Learn more1 resource

    deliveryMethod

    stringdefault: sync

    Determines how the API delivers task results.

    Allowed values3 values
    Returns complete results directly in the API response.
    Returns an immediate acknowledgment with the task UUID. Poll for results using getResponse.
    Streams results token-by-token as they are generated.
    Learn more1 resource

    includeCost

    booleandefault: false

    Include task cost in the response.

    includeUsage

    booleandefault: false

    Include token usage statistics in the response.

    Core Parameters

    Primary parameters that define the task output.

    numberResults

    integermin: 1max: 4default: 1

    Number of results to generate. Each result uses a different seed, producing variations of the same parameters.

    model

    stringrequiredvalue: minimax-m3

    Identifier of the model to use for generation.

    messages

    array of objectsrequiredmin items: 1

    Array of chat messages forming the conversation context.

    Properties2 properties
    messages » role

    role

    stringrequired

    The role of the message author.

    Allowed values2 values
    messages » content

    content

    stringrequiredmin: 1

    The text content of the message.

    tools

    array of objectsmin items: 1

    Tool definitions available for the model to call during generation.

    Properties4 properties
    tools » type

    type

    stringrequired

    The kind of tool to make available to the model. User-defined functions require name and schema, while built-in tools (search, codeInterpreter) are executed server-side by the provider.

    Allowed values1 value
    tools » name

    name

    stringmax: 64

    Unique function name. Required for function tools.

    tools » description

    description

    string

    Explanation of what the function does, used by the model to decide when to call it.

    tools » schema

    schema

    object

    JSON Schema object describing the function's input parameters.

    Settings

    Technical parameters to fine-tune the inference process. These must be nested inside the settings object.

    settings » systemPrompt

    systemPrompt

    stringmin: 1max: 200000

    System-level instruction that guides the model's behavior and output style across the entire generation.

    settings » temperature

    temperature

    floatmin: 0max: 2step: 0.01default: 1

    Controls randomness in generation. Lower values produce more deterministic outputs, higher values increase variation and creativity.

    settings » topP

    topP

    floatmin: 0max: 1step: 0.01default: 0.95

    Nucleus sampling parameter that controls diversity by limiting the probability mass. Lower values make outputs more focused, higher values increase diversity.

    settings » maxTokens

    maxTokens

    integermin: 1max: 512000default: 128000

    Maximum number of tokens to generate in the response.

    toolChoice

    object

    Controls how the model selects which tool to call. This only takes effect when tools are defined.

    Examples3 examples

    Let the model decide (default):

    "toolChoice": {
  "type": "auto"
}

    Force a specific tool call:

    "toolChoice": {
  "type": "tool",
  "name": "get_weather"
}

    Require any tool call:

    "toolChoice": {
  "type": "any"
}
    Properties2 properties
    toolChoice » type

    type

    stringrequired

    Strategy the model uses to decide when and which tools to call.

    Allowed values4 values
    The model decides whether to call a tool based on the conversation context. This is the recommended default.
    The model must call at least one tool but chooses which one. Useful when you always need structured output.
    The model must call the specific tool identified by name. Use this to force a particular function call.
    The model will not call any tool, even if tools are defined. Useful for forcing a text-only response.
    toolChoice » name

    name

    string

    Name of the specific tool the model must call. Required when type is tool.

    Ask AI
    Send feedback