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...
Harga
Bayar sesuai pemakaian
Tanpa biaya di muka, bayar hanya sesuai penggunaan
Coba model
Penggunaan API
Gunakan contoh kode berikut untuk integrasi dengan API kami:
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)Pengenalan Model
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
Fitur yang Didukung
Panduan Akses API
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
Konteks
1000K
Output Maks.
40K
Penggunaan Tool
Didukung
Coba Minimax M1 di WaveSpeedAI
Akses Minimax M1 melalui API terpadu kami — kompatibel dengan OpenAI, tanpa cold start, harga transparan.
Pertanyaan Umum tentang Minimax M1
Berapa biaya Minimax M1 melalui API?+
Harga di WaveSpeedAI: $0.40 per juta token input dan $2.20 per juta token output. Prompt caching dan batch processing ditagih terpisah dan mengurangi biaya efektif pada beban kerja yang panjang dan berulang.
Berapa context window Minimax M1?+
Minimax M1 mendukung hingga 1000K token konteks dengan hingga 40K token output per permintaan.
Apakah Minimax M1 kompatibel dengan OpenAI?+
Ya. WaveSpeedAI menyediakan Minimax M1 melalui endpoint yang kompatibel dengan OpenAI di https://llm.wavespeed.ai/v1. Arahkan OpenAI SDK resmi ke base URL ini dengan API key WaveSpeedAI Anda — tanpa perubahan kode lainnya.
Bagaimana memulai dengan Minimax M1?+
Masuk ke WaveSpeedAI, buat API key di Access Keys, lalu kirim permintaan ke https://llm.wavespeed.ai/v1/chat/completions dengan model id seperti ditampilkan di atas. Akun baru menerima kredit gratis untuk menguji Minimax M1.