MiniMax

MiniMax provides an OpenAI-compatible API for their language models. The MiniMax provider follows the OpenAI provider chat configuration pattern, with the MiniMax-specific parameter differences described below.

Setup

1. Get an API key from the MiniMax Platform
2. Set MINIMAX_API_KEY environment variable or specify apiKey in your config

Configuration

Basic configuration example:

providers:
  - id: minimax:MiniMax-M3
    config:
      temperature: 0.7
      max_completion_tokens: 2048
      apiKey: YOUR_MINIMAX_API_KEY

  - id: minimax:MiniMax-M2.7
    config:
      max_completion_tokens: 2048

Configuration Options

• temperature - Range [0, 2], with a vendor default of 1 when omitted
• max_completion_tokens - Maximum completion tokens. Legacy max_tokens config is translated to this field for compatibility. Limits depend on the model; see the MiniMax API reference.
• apiBaseUrl - Optional custom MiniMax-compatible proxy endpoint
• top_p
• tools and tool_choice - Use these for tool calling. MiniMax rejects the deprecated function_call parameter.

When MiniMax reports prompt-cache reads, promptfoo calculates cost using the returned cached token count and the model's cache-read rate.

Available Models

MiniMax-M3 (Default)

• Latest flagship model with up to a 1M token context window (512K guaranteed minimum) and up to 128K output
• Multimodal: supports text, image, and video input
• Standard tier, up to 512K input tokens: $0.06/1M cached input, $0.30/1M uncached input, and $1.20/1M output
• Standard tier, above 512K input tokens: $0.12/1M cached input, $0.60/1M uncached input, and $2.40/1M output
• Priority admission costs 1.5 times the standard tier; see pay-as-you-go pricing

note

M3 is the default. Compare the current API prices for your model, context length, and service tier before choosing a model for cost-sensitive workloads. Subscription Token Plans use a separate billing system.

MiniMax-M2.7

• Previous-generation flagship model
• 204,800 token context window
• Input: $0.06/1M (cache hit), $0.3/1M (cache miss)
• Output: $1.2/1M

MiniMax-M2.7-highspeed

• High-speed version of M2.7 for low-latency scenarios
• 204,800 token context window
• Input: $0.06/1M (cache hit), $0.6/1M (cache miss)
• Output: $2.4/1M

Example Usage

Here's an example comparing MiniMax with OpenAI:

providers:
  - id: minimax:MiniMax-M3
    config:
      temperature: 0.7
      max_completion_tokens: 2048
  - id: openai:gpt-4o
    config:
      temperature: 0.7
      max_tokens: 4000

prompts:
  - 'Answer the following question: {{question}}'

tests:
  - vars:
      question: 'What is the capital of France?'
    assert:
      - type: contains
        value: 'Paris'

API Documentation

• OpenAI Compatible API
• Anthropic Compatible API