What is MiniMax M3?

MiniMax M3 is an open-weight multimodal foundation model from MiniMax that brings together coding capability, agentic reasoning, native multimodality, and long-context processing in one model. It is designed for demanding AI workflows where a system needs to understand large amounts of information, reason through multi-step tasks, use tools, and work with different input types. MiniMax M3 supports a context window of up to 1 million tokens, making it useful for large code repositories, long documents, multi-file analysis, research workflows, enterprise automation, and persistent agent memory. The model uses MiniMax Sparse Attention, an architecture built to improve efficiency at very long context lengths by reducing the cost of attention. MiniMax M3 is natively multimodal and can work with text, images, and video inputs, allowing it to support richer workflows than text-only language models. It is positioned for coding, software engineering, tool invocation, browser-style retrieval, computer-use-style tasks, and autonomous task decomposition. The model’s architecture includes a large total parameter count with a smaller number of activated parameters, supporting more efficient inference through a mixture-of-experts design. Developers can use MiniMax M3 to build coding assistants, AI agents, document intelligence systems, multimodal analysis tools, and automated enterprise workflows. Its long-context design helps reduce the need to compress or split large inputs, allowing teams to keep more project context available during reasoning. The model is available through open-weight releases and hosted API providers, giving developers multiple ways to test, deploy, or integrate it into applications. MiniMax M3 helps organizations build advanced AI systems that combine long memory, multimodal understanding, coding strength, and agentic execution.

Pricing

Price Starts At:
$0.30 per million input tokens
Price Overview:
$0.30 per million input tokens and $1.20 per million output tokens
Free Version:
Free Version available.

Integrations

Offers API?:
Yes, MiniMax M3 provides an API

Screenshots and Video

MiniMax M3 Screenshot 1

Company Facts

Company Name:
MiniMax
Date Founded:
2021
Company Location:
Singapore
Company Website:
www.minimax.io

Product Details

Deployment
SaaS
Windows
Mac
Linux
On-Prem
Training Options
Documentation Hub
Support
Web-Based Support

Product Details

Target Company Sizes
Individual
1-10
11-50
51-200
201-500
501-1000
1001-5000
5001-10000
10001+
Target Organization Types
Mid Size Business
Small Business
Enterprise
Freelance
Nonprofit
Government
Startup
Supported Languages
English

MiniMax M3 Categories and Features

MiniMax M3 Customer Reviews

Write a Review
  • Reviewer Name: A Verified Reviewer
    Position: AI Developer
    Has used product for: Less than 6 months
    Uses the product: Weekly
    Org Size (# of Employees): 26 - 99
    Feature Set
    Ease Of Use
    Cost
    Would you Recommend to Others?
    1 2 3 4 5 6 7 8 9 10

    MiniMax M3 review

    Date: Aug 12 2026
    Summary

    M3 feels like a serious model for developers who care about long context, coding, multimodal inputs, and agentic workflows. It is not just another chatbot model with coding pasted on top; it feels built for the kind of complex, context-heavy work modern AI agents actually need to do.

    Positive

    The multimodal side also makes it more flexible than a code-only model. Being able to work across text, images, video-style understanding, and code gives it more room to support real workflows instead of being boxed into one narrow use case.

    For agent builders, the best part is that M3 is clearly designed around tool use and multi-step execution. MiniMax specifically calls out autonomous task decomposition and tool invocation, which is exactly what matters when a model is powering coding assistants, workflow agents, or automated dev tools.

    Negative

    The main catch is infrastructure and trust. Even with sparse attention and MoE efficiency, this is still a large model, so deployment is not casual. I would also want to benchmark it on my own repos before depending on it for production work.

    Read More...
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