minimax/minimax-m1
1,000,000 context · $0.40/M input tokens · $2.20/M output tokens
MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it...
Pricing
Pay-per-use
No upfront costs, pay only for what you use
Try the model
API Usage
Use the following code examples to integrate with our API:
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://llm.wavespeed.ai/v1"
)
response = client.chat.completions.create(
model="minimax/minimax-m1",
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(response.choices[0].message.content)Model Introduction
Minimax minimax-m1
MiniMax-M1 is a high-performance large language model optimized for efficiency and accuracy in diverse tasks
MiniMax-M1 is a high-performance large language model optimized for efficiency and accuracy in diverse tasks.
Why It Looks Great
- MoE (Mixture of Experts) architecture for efficient processing
- 1000000 context window for long document handling
- Competitive pricing at $0.4/$2.4 per million tokens
Key Features
- Context Window: 1000000 tokens
- Max Output: 40000 tokens
- Vision: Supported
- Function Calling: Supported
Specifications
| Specification | Value |
|---|---|
| Provider | Minimax |
| Model Type | Large Language Model (LLM) |
| Architecture | MoE (Mixture of Experts) |
| Context Window | 1000000 tokens |
| Max Output | 40000 tokens |
| Input | Text |
| Output | Text |
| Vision | Supported |
| Function Calling | Supported |
Pricing
| Token Type | Cost per Million Tokens |
|---|---|
| Input | $0.4 |
| Output | $2.4 |
How to Use
- Write your prompt — describe the task, provide context, and specify desired output format.
- Submit — the model processes your request and returns the response.
API Integration
Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: minimax/minimax-m1
API Usage
Python SDK
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://llm.wavespeed.ai/v1"
)
response = client.chat.completions.create(
model="minimax/minimax-m1",
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(response.choices[0].message.content)cURL
curl https://llm.wavespeed.ai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "minimax/minimax-m1",
"messages": [{"role": "user", "content": "Hello!"}]
}'Notes
- Model: minimax/minimax-m1
- Provider: Minimax
Info
Supported Functionality
API Access Guide
Minimax M1 API
minimax/minimax-m1
MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it...
Input
$0.4 /M
Output
$2.2 /M
Context
1000K
Max Output
40K
Tool Use
Supported
Try Minimax M1 on WaveSpeedAI
Access Minimax M1 through our unified API — OpenAI-compatible, no cold starts, transparent pricing.
Frequently Asked Questions about Minimax M1
How much does Minimax M1 cost via the API?+
Pricing on WaveSpeedAI: $0.40 per million input tokens and $2.20 per million output tokens. Prompt caching and batch processing are billed separately and reduce effective cost on long, repetitive workloads.
What is the context window of Minimax M1?+
Minimax M1 supports up to 1000K tokens of context with up to 40K tokens of output per request.
Is Minimax M1 OpenAI-compatible?+
Yes. WaveSpeedAI exposes Minimax M1 through an OpenAI-compatible endpoint at https://llm.wavespeed.ai/v1. Point the official OpenAI SDK at this base URL with your WaveSpeedAI API key — no other code changes required.
How do I get started with Minimax M1?+
Sign in to WaveSpeedAI, create an API key in Access Keys, then send a request to https://llm.wavespeed.ai/v1/chat/completions with model id set to the value shown above. New accounts receive free credits to evaluate Minimax M1 before paying per token.