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const { OpenAI } = require('openai');
const api = new OpenAI({
baseURL: 'https://api.ai.cc/v1',
apiKey: '',
});
const main = async () => {
const result = await api.chat.completions.create({
model: 'minimax/m2',
messages: [
{
role: 'system',
content: 'You are an AI assistant who knows everything.',
},
{
role: 'user',
content: 'Tell me, why is the sky blue?'
}
],
});
const message = result.choices[0].message.content;
console.log(`Assistant: ${message}`);
};
main();
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.ai.cc/v1",
api_key="",
)
response = client.chat.completions.create(
model="minimax/m2",
messages=[
{
"role": "system",
"content": "You are an AI assistant who knows everything.",
},
{
"role": "user",
"content": "Tell me, why is the sky blue?"
},
],
)
message = response.choices[0].message.content
print(f"Assistant: {message}")
-
AI Playground
Test all API models in the sandbox environment before you integrate.
We provide more than 300 models to integrate into your app.
Product Detail
✨ Discover MiniMax M2 API: Advanced Agent-Based Reasoning
The MiniMax M2 API represents a leap in AI technology, offering a state-of-the-art agent-based reasoner built for unparalleled efficiency and advanced autonomous capabilities. Leveraging a cutting-edge Mixture of Experts (MoE) architecture, MiniMax M2 optimizes performance by activating 10 billion parameters per inference from a massive 230 billion total parameters.
This design ensures significantly reduced latency and high throughput, making it the ideal choice for demanding, real-time AI applications requiring sophisticated decision-making and rapid execution.
⚙️ Technical Specifications
- • Architecture: Mixture of Experts (MoE)
- • Total Parameters: 230 billion
- • Active Parameters: 10 billion (per inference)
- • Latency: Optimized for real-time applications
- • Throughput: High, supporting large-scale deployments
- • Model Type: Agent-based reasoner
🚀 Unrivaled Performance Benchmarks
- ✅ Outperforms leading open models like Claude Opus and Gemini 2.5 in independent benchmark tests.
- ✅ Ranked among the Top 5 smartest AI models globally by Artificial Intelligence rankings.
- ✅ Demonstrates superior reasoning, coding, and prompt generation capabilities across diverse evaluation scenarios.
💡 Key Features for Autonomous AI
- • Agent-based Reasoning: Drives autonomous, context-aware decision-making for complex tasks.
- • Advanced Coding Capabilities: Writes, debugs code, and crafts effective prompts for autonomous AI agents.
- • Efficient MoE Design: Enables low-latency responses, even with a large parameter count, maximizing speed and responsiveness.
- • Robust Scalability: Balances performance and scalability for deployment across various cloud and edge environments.
- • Seamless Integration: Supports integration with autonomous systems requiring complex reasoning workflows.
💰 MiniMax M2 API Pricing
Experience powerful AI capabilities with competitive pricing:
- • Input Tokens: $0.315 per 1M tokens
- • Output Tokens: $1.26 per 1M tokens
🎯 Practical Use Cases
- • Autonomous AI Agent Orchestration: Efficiently manage and control complex AI agent systems.
- • Automated Coding Assistance: Support software developers with bug fixing, code generation, and optimization.
- • Advanced Prompt Generation: Create highly effective prompts for AI-driven workflows and task automation.
- • Real-time Decision Support: Implement intelligent systems for dynamic environments requiring immediate insights.
- • Multi-Agent Systems R&D: Accelerate research and development in sophisticated multi-agent AI ecosystems.
💻 Code Sample for Easy Integration
Integrating MiniMax M2 into your applications is straightforward. Below is an example of how you might interact with the API:
import openai
client = openai.OpenAI(api_key="YOUR_API_KEY", base_url="https://api.minimax.com/v1")
response = client.chat.completions.create(
model="minimax/m2",
messages=[
{"role": "system", "content": "You are an expert autonomous agent."},
{"role": "user", "content": "Generate a Python function to sort a list of numbers efficiently."}
]
)
print(response.choices[0].message.content)
For detailed API documentation and more code examples, please refer to the official MiniMax M2 API documentation.
🆚 MiniMax M2: Comparative Analysis
Understand how MiniMax M2 stands out against other prominent AI models:
- • vs Claude Opus: MiniMax M2 excels in swift, scalable agent reasoning with strong coding-centric capabilities. While Claude Opus aims for maximal reasoning depth and complex multi-step problem solving with robust tool and memory integration, MiniMax M2 offers a more focused and efficient approach for autonomous agent tasks.
- • vs Gemini 2.5: MiniMax M2 surpasses Gemini 2.5 Pro in overall intelligence scores, ranking among the top five globally. Its MoE architecture provides superior efficiency by activating a smaller subset of parameters per inference. Gemini 2.5 shines in multimodal creativity (generating images, art, audio), whereas MiniMax M2's strength lies in reasoning, coding, and autonomous agent tasks.
- • vs PaLM 2: While PaLM 2 is known for its multilingual, coding, and research capabilities, MiniMax M2 outperforms it in open intelligence rankings and autonomous reasoning benchmarks. PaLM 2's versatility covers a wide range of applications, but MiniMax M2 distinguishes itself with cost-efficient, high-performance agent reasoning for specialized tasks.
- • vs GPT-4: GPT-4 remains a leader in broad multilingual understanding and diverse domains. MiniMax M2, however, specifically optimizes those domains tied to autonomous agent reasoning, coding, and deep search functions, offering a highly specialized and performant alternative in these areas.
❓ Frequently Asked Questions (FAQ)
Q1: What makes MiniMax M2 unique for AI development?
MiniMax M2 stands out due to its agent-based reasoning and Mixture of Experts (MoE) architecture, delivering low-latency, high-throughput performance specifically optimized for autonomous systems, complex coding, and advanced prompt generation.
Q2: How does MiniMax M2 ensure high efficiency?
Its MoE architecture allows it to activate only 10 billion parameters per inference from a total of 230 billion. This intelligent resource allocation significantly reduces computational overhead, leading to superior speed and cost-efficiency compared to models that activate all parameters.
Q3: Can MiniMax M2 handle complex coding tasks?
Yes, MiniMax M2 is highly capable in coding. It can write, debug, and optimize code, making it an invaluable tool for software developers and for automating code-centric workflows within autonomous agents.
Q4: How does MiniMax M2 compare to industry leaders like GPT-4?
While GPT-4 offers broad general intelligence, MiniMax M2 is specifically engineered to excel in specialized domains such as autonomous agent reasoning, coding, and deep search, providing optimized performance and efficiency for these targeted applications.
Q5: Is MiniMax M2 suitable for real-time applications?
Absolutely. MiniMax M2 is designed for low latency and high throughput, making it perfectly suited for real-time decision support systems, live agent orchestration, and other dynamic environments where rapid response times are critical.
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