MiniMax M2.1 Guide: Digital Employee for AI Coding

01What Is MiniMax M2.1

MiniMax M2.1 represents a fundamental shift in how we think about AI coding assistants. Released December 23, 2025, it's not just another model optimized for chat - it's designed from the ground up to be a "Digital Employee" capable of handling end-to-end workflows in real production environments.

The key innovation is efficiency: M2.1 uses a Mixture-of-Experts (MoE) architecture with 230 billion total parameters but only activates 10 billion per token. This means you get access to the knowledge of a 230B model at the inference cost of a 10B model - making it exceptionally fast and affordable for the rapid-fire cycles of agentic workflows.

02Company Background: MiniMax

MiniMax is part of China's "AI Tigers" - the leading AI startups alongside DeepSeek, Zhipu (Z.ai), Baichuan, and Moonshot/Kimi. Founded in December 2021 and headquartered in Shanghai, MiniMax has rapidly grown to a $4 billion valuation with backing from tech giants and strategic investors.

Product Portfolio

03Technical Specifications

Architecture Deep Dive

04Key Improvements Over M2

M2 (released October 2025) focused on cost and accessibility. M2.1 shifts focus to real-world complex tasks - particularly usability across more programming languages and office scenarios.

05Benchmark Performance

Software Engineering Benchmarks

VIBE Benchmark: A New Standard

Framework Generalization

M2.1 was specifically evaluated across multiple coding agent frameworks, demonstrating exceptional stability:

Also supports context management conventions: Skill.md, Claude.md/agent.md/.cursorrule, and Slash Commands.

06Digital Employee Capabilities

The "Digital Employee" is M2.1's signature feature - moving beyond coding assistance to full office automation. It accepts web content in text form and controls mouse clicks and keyboard inputs via text-based commands.

Showcase Demonstrations

MiniMax provides interactive demos showing M2.1's capabilities:

07Pricing & Access

API Pricing Comparison

Cost Comparison Example

Access Methods

08Getting Started

Claude Code Integration

{
  "apiProvider": "openrouter",
  "openRouterApiKey": "your-openrouter-key",
  "apiModelId": "minimax/minimax-m2.1",
  "customInstructions": "Use Interleaved Thinking for complex tasks"
}

API Quick Start

import openai

client = openai.OpenAI(
    api_key="your-minimax-api-key",
    base_url="https://api.minimax.io/v1"
)

response = client.chat.completions.create(
    model="minimax-m2.1",
    messages=[
        {"role": "user", "content": "Build a React component for a todo list"}
    ],
    temperature=1.0,
    top_p=0.95
)

print(response.choices[0].message.content)

Hardware Requirements for Local Deployment

09When to Use MiniMax M2.1

M2.1 vs GLM-4.7 vs Kimi K2