Technical Specifications of minimax-m2
| Specification | Details |
|---|---|
| Model ID | minimax-m2 |
| Provider | MiniMax |
| Model type | Large language model |
| Core positioning | Compact, efficient model for programming and agent workflows |
| Active parameters | 10 billion |
| Total parameters | 230 billion |
| Primary strengths | Code generation, multi-file editing, compile-run-fix loops, defect repair, tool use, multi-step task execution |
| Performance profile | Near state-of-the-art in general inference, tool use, and agent-style execution with low latency |
| Benchmark highlights | SWE-Bench Verified, Multi-SWE-Bench, Terminal-Bench, BrowseComp, GAIA |
| Deployment advantages | Fast inference, high concurrency, strong unit economics, scalable deployment efficiency |
| Best-fit use cases | Large-scale agents, developer assistants, inference-driven applications, cost-sensitive production systems |
What is minimax-m2?
minimax-m2 is a compact and efficient large language model designed for end-to-end programming tasks and agent-oriented workflows. It uses 10 billion active parameters out of 230 billion total parameters, giving it a strong balance between capability, latency, and deployment efficiency.
The model is built to perform well across code-heavy and execution-based scenarios, especially where systems need to reason through multiple steps, use tools effectively, and recover from errors during longer task chains. It is particularly well suited for environments that require code generation, repository-level changes, iterative debugging, and test-driven repair.
minimax-m2 also stands out for combining strong general intelligence with practical operational efficiency. It has shown competitive results in benchmarks covering software engineering, terminal execution, browsing-based problem solving, and agent planning, while its relatively small active parameter footprint makes it attractive for high-throughput and cost-efficient deployment.
Main features of minimax-m2
- Compact active architecture: With 10 billion active parameters and 230 billion total parameters,
minimax-m2is optimized to deliver strong capability while keeping inference fast and efficient. - Programming-focused performance: The model excels in code generation, multi-file editing, compile-run-fix loops, and defect repair, making it highly effective for software engineering workflows.
- Agent workflow optimization:
minimax-m2is designed for tool use, multi-step execution, and long-horizon task handling, which makes it suitable for autonomous and semi-autonomous agent systems. - Strong benchmark competitiveness: It performs well on evaluations such as SWE-Bench Verified, Multi-SWE-Bench, Terminal-Bench, BrowseComp, and GAIA, indicating broad utility across coding and agent tasks.
- General reasoning capability: Beyond coding, the model is rated in the top tier of open-source systems in areas such as mathematics, scientific inference, and instruction following.
- Low-latency deployment: Its efficient active parameter count supports faster response times, better concurrency, and improved production economics.
- Scalable for production use:
minimax-m2is a strong fit for large-scale agent deployment, developer support tools, and applications that need both responsiveness and cost control.
How to access and integrate minimax-m2
Step 1: Sign Up for API Key
To get started, sign up on the CometAPI platform and generate your API key from the dashboard. Once you have your API credentials, you can use them to authenticate requests to the minimax-m2 model.
Step 2: Send Requests to minimax-m2 API
After obtaining your API key, send requests to CometAPI’s compatible chat completions endpoint and specify minimax-m2 as the model name.
curl https://api.cometapi.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_API_KEY" \
-d '{
"model": "minimax-m2",
"messages": [
{
"role": "user",
"content": "Write a Python function that checks whether a string is a palindrome."
}
]
}'
from openai import OpenAI
client = OpenAI(
api_key="YOUR_COMETAPI_API_KEY",
base_url="https://api.cometapi.com/v1"
)
response = client.chat.completions.create(
model="minimax-m2",
messages=[
{"role": "user", "content": "Write a Python function that checks whether a string is a palindrome."}
]
)
print(response.choices[0].message.content)
Step 3: Retrieve and Verify Results
Once the API returns a response, extract the generated content from the response object and validate it for your application. For production workflows, you may also want to add logging, schema validation, retries, guardrails, and automated evaluation to verify that minimax-m2 is producing consistent and reliable outputs.