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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/m1',
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/m1",
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
MiniMax M1: The Powerhouse for Extreme Scale AI
The MiniMax M1 is an advanced, open-weight Mixture-of-Experts (MoE) transformer engineered for unparalleled performance in demanding AI applications. Boasting an impressive 456 billion total parameters (with 45B active per token) and an expansive 1 million token context window, M1 is built to handle the most complex data analysis tasks.
With an exceptional 80,000 token output capacity, it excels at massive input processing, intricate logical analysis, and deep code reasoning. MiniMax M1 is the ideal solution for sophisticated RAG pipelines, intensive legal and scientific workflows, and advanced agentic tools requiring extreme context and analytical depth.
🔍 Technical Specifications
Core Parameters:
- ✓ Context Window: 1,000,000 tokens
- ✓ Output Capacity: Up to 80,000 tokens
- ✓ Architecture: Sparse MoE Transformer with Lightning Attention
- ✓ Parameters: 456B total (45B active per token)
💰 API Pricing Structure:
Input Tokens (per million tokens):
- • 0–200k tokens: $0.42
- • 200k+ tokens: $1.365 (tiered pricing)
Output Tokens (per million tokens):
- • All tiers: $2.31
📊 Performance Metrics
✨ Key Capabilities of MiniMax M1
- 📁 Full-Scale Comprehension: Seamlessly processes vast documents and entire codebases across million-token inputs.
- ⚡ Fast & Optimized Inference: Leverages efficient MoE routing for rapid processing.
- 🔧 Efficient Serving & Compatibility: Designed for streamlined deployment and broad system compatibility.
- 🧰 Advanced Agentic Workflows: Robust support for tool use and sophisticated planning in AI agents.
🎯 Optimal Use Cases
💻 Code Engineering
Process and refactor extensive code repositories in a single, comprehensive pass, enhancing developer productivity.
📜 Document Analytics
Perform deep reasoning and extraction over complex legal, technical, and regulatory data sets.
🔍 RAG Systems
Serves as a powerful, long-context backend for advanced Retrieval-Augmented Generation (RAG) question-answering systems.
📈 Mathematical Reasoning
Enables robust, step-by-step symbolic and logical analysis for intricate mathematical problems.
💻 Code Samples
Integrating MiniMax M1 into your projects is straightforward. Below is an example snippet for API interaction:
import openai # Assuming compatibility with OpenAI's client
client = openai.OpenAI(
base_url="https://api.example.com/v1", # Replace with actual MiniMax M1 API base URL
api_key="YOUR_MINIMAX_API_KEY",
)
chat_completion = client.chat.completions.create(
model="minimax/m1",
messages=[
{"role": "system", "content": "You are a helpful AI assistant."},
{"role": "user", "content": "Analyze this large legal document for key clauses."},
],
max_tokens=80000,
temperature=0.7,
)
print(chat_completion.choices[0].message.content)
(Note: Replace base_url and api_key with your actual MiniMax M1 API credentials.)
📊 Comparison with Other Leading Models
Vs. GPT-4o
MiniMax M1 offers a significantly larger 1M context tokens compared to GPT-4o’s 128K, making M1 superior for processing exceptionally large inputs.
Vs. Claude 4 Opus
While both excel in reasoning, M1 provides a broader context window of 1M tokens against Claude 4 Opus’s 128K, ideal for extreme long-document analysis.
Vs. Gemini 2.5 Pro
MiniMax M1 takes the lead in total token capacity and overall scale, particularly for highly structured and extensive input data processing.
⚠ Current Limitations
- ⚠ No Vision/Multimodal Support: M1 currently focuses solely on text-based inputs and outputs.
- ⚠ No Fine-Tuning API: Direct fine-tuning capabilities are not exposed via API at this time.
- ⚠ Manual Integration for Some Tools: Certain third-party tools or platforms may require custom integration efforts.
🔗 API Integration & Documentation
The MiniMax M1 model is readily accessible via its dedicated AI/ML API. Comprehensive documentation is available to assist developers with integration, configuration, and best practices.
Access Documentation Here: MiniMax M1 API Reference
❓ Frequently Asked Questions (FAQ)
Q1: What makes MiniMax M1 unique compared to other large language models?
MiniMax M1 stands out with its industry-leading 1 million token context window and 80,000 token output capacity, powered by a 456B parameter Sparse MoE Transformer. This allows it to handle significantly larger inputs and generate more extensive outputs, making it ideal for deep, complex analytical tasks across massive datasets.
Q2: What are the primary use cases for MiniMax M1?
M1 is optimally designed for applications requiring extreme context and reasoning. Key use cases include advanced code engineering (refactoring large repositories), in-depth document analytics (legal, scientific, regulatory), robust RAG systems as a long-context backend, and complex mathematical and logical reasoning.
Q3: Does MiniMax M1 support multimodal inputs like images or audio?
No, MiniMax M1 is currently a text-only model. It does not support vision or other multimodal input types.
Q4: How does M1's pricing work for API usage?
Pricing for MiniMax M1 API is tiered for input tokens: $0.42 per million for the first 200k tokens, and $1.365 per million for tokens beyond 200k. Output tokens are consistently priced at $2.31 per million across all tiers.
Q5: Is there a fine-tuning API available for MiniMax M1?
At present, MiniMax M1 does not expose a public API for fine-tuning. Users should integrate the model as provided for their specific tasks.
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