minimax/minimax-m2.5
minimax/minimax-m2.5
204,800 context · $0.30/M input tokens · $1.20/M output tokens
MiniMax-M2.5 is a SOTA large language model designed for real-world productivity. Trained in a diverse range of complex real-world digital working environments, M2.5 builds upon the coding expertise of M2.1 to extend into general office work, reaching fluency in generating and operating Word, Excel, and Powerpoint files, context switching between diverse software environments, and working across different agent and human teams. Scoring 80.2% on SWE-Bench Verified, 51.3% on Multi-SWE-Bench, and 76.3% on BrowseComp, M2.5 is also more token efficient than previous generations, having been trained to optimize its actions and output through planning.
料金
従量課金
初期費用なし、使った分だけお支払い
API 利用
以下のコード例を使用して API と連携してください:
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-m2.5",
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(response.choices[0].message.content)モデル紹介
Minimax minimax-m2.5
MiniMax-M2
MiniMax-M2.5 is a SOTA large language model designed for real-world productivity, excelling in coding, office work, and agentic workflows.
Why It Looks Great
- MoE (Mixture of Experts) architecture for efficient processing
- 204800 context window for long document handling
- Competitive pricing at $0.3/$1.2 per million tokens
Key Features
- Context Window: 204800 tokens
- Max Output: N/A tokens
- Vision: Supported
- Function Calling: Supported
Specifications
| Specification | Value |
|---|---|
| Provider | Minimax |
| Model Type | Large Language Model (LLM) |
| Architecture | MoE (Mixture of Experts) |
| Context Window | 204800 tokens |
| Max Output | tokens |
| Input | Text |
| Output | Text |
| Vision | Supported |
| Function Calling | Supported |
Pricing
| Token Type | Cost per Million Tokens |
|---|---|
| Input | $0.3 |
| Output | $1.2 |
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-m2.5
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-m2.5",
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-m2.5",
"messages": [{"role": "user", "content": "Hello!"}]
}'Notes
- Model: minimax/minimax-m2.5
- Provider: Minimax