MiniMax M2.1 Launch: Advancing Multi-Language Programming for Real-World Tasks

We are proud to introduce MiniMax M2.1 on Atlas Cloud, a model engineered to handle complex, real-world programming tasks with superior multi-language support and agentic capabilities. 

• Input: $0.3 per 1 million tokens.
• Output: $1.2 per 1 million tokens.

This service structure positions MiniMax M2.1 as an optimal solution for developers requiring near frontier-level performance combined with the control of open-source models, predictable costs, and the ease of an OpenAI-compatible interface.

Key Highlights of MiniMax M2.1

Comprehensive Multi-Language Engineering

The model expands programming proficiency beyond Python to cover the entire software development lifecycle systematically.

• Language Diversity: It supports enhanced capabilities in Rust, Java, Golang, C++, Kotlin, Objective-C, TypeScript, and JavaScript.
• Composite Instructions: The system adheres to complex "composite instruction constraints" for accurate logic execution.
• General Writing: It provides detailed and structured responses for technical documentation and daily dialogue.

This broad technical coverage benefits system architects who require consistency from low-level drivers to application layers, effectively rivaling the performance of Claude Sonnet 4.5 in multilingual tasks.

Optimized Efficiency and Tool Integration

MiniMax M2.1 improves operational speed while integrating seamlessly with modern development environments and agent frameworks.

• Resource Management: It reduces token consumption and improves speed through concise thought chains.
• Framework Support: The model works stably with tools like Claude Code, Droid, Cline, and Roo Code.
• Context Handling: It manages context mechanisms including Skill.md, agent.md, and Slash Commands. These improvements make the model ideal for developers using agent-driven workflows who need faster and more cost-effective interactions than previous iterations.

Advanced Visual and Mobile Capabilities

The model addresses industry gaps by enhancing design comprehension and native mobile coding skills.

• Mobile Platforms: It strengthens capabilities for native Android and iOS development.
• Aesthetic Logic: The system understands design principles for constructing complex web interactions.
• Simulation: It handles 3D scientific scene simulations and high-quality visualization. Frontend engineers can utilize these features to produce functional applications that score highly on benchmarks like VIBE-Web and VIBE-Android.

Applications in Real-World Scenarios

Large-Scale System Maintenance and Development

Enterprise teams can leverage the model to manage complex legacy codebases and new infrastructure projects.

• Multi-Language Stacks: It assists in environments mixing Java, Go, and C++ for backend logic.
• Code Review: The system conducts reviews and generates test cases for rigorous quality assurance. This application suits backend engineers needing reliable support that outperforms the standard M2 model on software engineering leaderboards.

Case: rewrite codes with Multi-Language

#Prompt: Rewrite it with java & rewrite it with C++:
#!/usr/bin/python
# -*- coding: UTF-8 -*-
 
for i in range(1,5):
    for j in range(1,5):
        for k in range(1,5):
            if( i != k ) and (i != j) and (j != k):
                print (i,j,k)
                
 #output:
 #Cpp
 #include <iostream>
int main() {
    for (int i = 1; i < 5; i++) {
        for (int j = 1; j < 5; j++) {
            for (int k = 1; k < 5; k++) {
                if (i != k && i != j && j != k) {
                    std::cout << i << " " << j << " " << k << std::endl;
                }
            }
        }
    }
    return 0;
}
 #Java
 public class PermutationGenerator {
    public static void main(String[] args) {
        for (int i = 1; i < 5; i++) {
            for (int j = 1; j < 5; j++) {
                for (int k = 1; k < 5; k++) {
                    if (i != k && i != j && j != k) {
                        System.out.println(i + " " + j + " " + k);
                    }
                }
            }
        }
    }
}

End-to-End Application Construction

Developers can build complete applications from the ground up with a strong focus on visual quality and interaction.

• Interactive UI: It builds complex frontend interactions for modern web platforms.
• Mobile Deployment: The model generates deployable code for specific Android and iOS requirements. This scenario benefits independent developers and startups aiming to achieve a "zero to one" product launch with high aesthetic standards.

Case：Web 3D Lego Sandbox（Click to see the video）

Continuous Agent-Driven Workflows

The model serves as a reliable backbone for automated coding agents and continuous integration tools.

• Instruction Following: It maintains stability over long-horizon tasks without losing context.
• Tool Execution: The system executes commands within scaffolding like Roo Code or BlackBox. This utility provides significant value to automation engineers seeking stability comparable to Claude Opus 4.5 in complex task execution.

case: example prompt to vibe coding with Atlas Cloud

"Write a Python script using the openai SDK, but configured to point to AtlasCloud's API endpoint (https://api.atlascloud.ai/v1). The script should define a function vibe_to_code(user_vibe) that takes a high-level, abstract description (e.g., 'make it pop with neon colors') and uses the MiniMax-M2.1 model to interpret this vibe and generate the corresponding CSS and HTML code."

Conclusion

MiniMax M2.1 combines extensive language support with efficient processing to solve real-world programming challenges. We encourage users to access these advanced capabilities and experience the improved efficiency directly in our Atlas Cloud playground.

How to use MiniMax M2.1 on Atlas Cloud?

Atlas Cloud lets you use MiniMax M2.1 first in a playground, then via a single API.

Method 1: Use directly in Atlas Cloud playground

Try MiniMax M2.1 in the playground.

Method 2: Access via API

Step 1: Get your API key

Create an API key in your console and copy it for later use.

Step 2: Check the API documentation

Review the endpoint, request parameters, and authentication method in our API docs.

Step 3: Make your first request (Python example)

Example: send a request with MiniMax M2.1.

import requests

url = "https://api.atlascloud.ai/v1/chat/completions"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "minimaxai/minimax-m2.1",
    "messages": [
        {
            "role": "user",
            "content": "what is difference between http and https"
        }
    ],
    "max_tokens": 65536,
    "temperature": 1,
    "stream": True
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

FAQ

How does MiniMax M2.1 improve upon the previous M2 model? MiniMax M2.1 expands beyond Python optimization to offer systematic improvements in languages like Rust, Java, Go, and C++. It also introduces stronger capabilities for native mobile development (iOS/Android) and provides more concise responses to reduce token consumption and latency.

What makes this model suitable for "Vibe Coding" or frontend work? The model has been trained with a specific focus on design comprehension and aesthetic expression. It excels at generating complex interactive logic for Web and App scenarios, achieving high scores on the VIBE benchmark which evaluates visual and interactive quality rather than just text correctness.

How does the model compare to competitors like Claude Sonnet 4.5? In software engineering benchmarks, MiniMax M2.1 outperforms Claude Sonnet 4.5 in multilingual scenarios and specific domains like code review and test case generation. It closely approaches the performance of Claude Opus 4.5 while offering faster response speeds.