MiniMax-M1 is a large-scale, open-weight reasoning model with 456B total parameters and 45.9B active per token, leveraging a hybrid Mixture-of-Experts (MoE) architecture and a custom "lightning attention" mechanism. It supports context windows up to 1 million tokens and is optimized for long-context understanding, software engineering, agentic tool use, and mathematical reasoning. The model is trained via a custom reinforcement learning pipeline (CISPO) and demonstrates strong performance on FullStackBench, SWE-bench, MATH, GPQA, and TAU-Bench.
Pricing
Pay-as-you-go rates for this model. More details can be found here.
Input Tokens (1M)
$0.40
Output Tokens (1M)
$0.96
Capabilities
Input Modalities
Output Modalities
Supported Parameters
Available parameters for API requests
Usage Analytics
Token usage of this model on our platform
Throughput
Time-To-First-Token (TTFT)
Not enough TTFT data
Code Example
Example code for using this model through our API with Python (OpenAI SDK) or cURL. Replace placeholders with your API key and model ID.
Basic request example. Ensure API key permissions. For more details, see our documentation.
from openai import OpenAI
client = OpenAI(
base_url="https://api.naga.ac/v1",
api_key="YOUR_API_KEY",
)
resp = client.chat.completions.create(
model="minimax-m1",
messages=[
{{"role": "user", "content": "What's 2+2?"}}
],
temperature=0.2,
)
print(resp.choices[0].message.content)