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MiniMax M3 logo

MiniMax M3

MiniMax M3 is an open-weight, natively multimodal frontier model with ~428B total parameters and ~23B activated parameters. It combines frontier-level coding and agentic performance, an ultra-long context window of up to 1M tokens, and mixed-modality training across text, image, and video.

Deploy dedicated endpoint
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Example Usage

Output

  Speculative decoding uses a smaller draft model to predict multiple
  tokens ahead, then verifies them against the full model in a single
  pass. Accepted tokens skip individual generation steps, improving
  throughput without sacrificing accuracy. It's most effective when the
  draft model closely matches the target model's distribution.
Code to use
Python

  from openai import OpenAI
  
  client = OpenAI(
      base_url="https://model.api.modular.com",
      api_key="<your_api_token>",
  )
  
  response = client.chat.completions.create(
      model="minimax/minimax-m3",
      messages=[
          {"role": "system", "content": "You are a helpful assistant."},
          {"role": "user", "content": "Explain speculative decoding in 3 sentences."},
      ],
      stream=True,
  )
  
  for chunk in response:
      if chunk.choices[0].delta.content:
          print(chunk.choices[0].delta.content, end="")
Get your API token
Output

  “The image shows a fox wearing a backpack in a forest.”
Input image
Cute orange cartoon fox with a small brown backpack sitting on grass in a bright, colorful forest.
Code to use

  import base64
  from openai import OpenAI
  
  client = OpenAI(
      base_url="https://model.api.modular.com",
      api_key="<your_api_token>",
  )
  
  with open("fox.png", "rb") as image_file:
      image_data = base64.b64encode(image_file.read()).decode("utf-8")
  
  response = client.chat.completions.create(
      model="minimax/minimax-m3",
      messages=[
          {
              "role": "user",
              "content": [
                  {
                      "type": "text",
                      "text": "Describe this image in one sentence.",
                  },
                  {
                      "type": "image_url",
                      "image_url": {
                          "url": f"data:image/png;base64,{image_data}"
                      },
                  },
              ],
          }
      ],
  )
  
  print(response.choices[0].message.content)
Model Details
  • Developed by
    MiniMax
  • Model family
    minimax/minimax-m3
  • Modality
    LLM,
    Vision,
  • Context Window
    1M
  • Total Params
    428B
  • Precision
    FP16, F32
  • Deployment options
    Shared, Dedicated, Self-hosted

Why choose MiniMax M3 on Modular?

  • High performance, out of the box

    Run leading open models with strong default performance and the ability to optimize down to the kernel — extracting more from every GPU.

  • Lower Infrastructure Costs

    Deploy efficiently across NVIDIA and AMD hardware to reduce GPU count, increase throughput, and avoid expensive closed-model licensing.

  • Easy Integration

    Integrate through an OpenAI-compatible endpoint, swap models freely, and scale across clouds or hardware without redesigning your application stack.

MiniMax M3
Want to self-host this model with our open source infrastructure?
Read How

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