MiniMax M2.1
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MiniMax M2.1

minimax-m2.1
Minimax
MiniMax-M2.1 redefines efficiency for intelligent agents. It is a compact, fast, and cost-effective MoE model with a total of 230 billion parameters and 10 billion active parameters, designed for top performance in coding and intelligent agent tasks while maintaining strong general intelligence. With only 10 billion active parameters, MiniMax-M2 delivers the complex end-to-end tool usage performance expected from today's leading models, but in a more streamlined form factor, making deployment and scaling easier than ever before.

Pricing

  • Input Tokens: $0.288 /M tokens
  • Output Tokens: $1.152 /M tokens

Input Modalities

  • Text

Output Modalities

  • Text

Capabilities

  • Thinking
  • Tools
  • Tool calling
  • Structured outputs

Providers

Baidu baidu-minimax-m2.1
Pricing$0.288$1.152
Context200K
Max output192K
Latency-
Throughput-
Uptime
0.00% uptime 3 days ago
0.00% uptime 2 days ago
0.00% uptime yesterday
Minimax mm-minimax-m2.1
Pricing$0.288$1.152
Context204K
Max output192K
Latency-
Throughput-
Uptime
0.00% uptime 3 days ago
0.00% uptime 2 days ago
0.00% uptime yesterday

Performance for minimax-m2.1

Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).

Uptime
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Latency
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Throughput
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Try this model

Python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIHUBMIX_API_KEY"],
    base_url="https://aihubmix.com/v1",
)

response = client.chat.completions.create(
    model="minimax-m2.1",
    messages=[
      {
        "role": "user",
        "content": "Hello, how are you?"
      }
    ],
    max_tokens=1024,
    stream=False,
)

print(response.choices[0].message.content)

Frequently asked questions

What is MiniMax M2.1?

MiniMax-M2.1 redefines efficiency for intelligent agents. It is a compact, fast, and cost-effective MoE model with a total of 230 billion parameters and 10 billion active parameters, designed for top performance in coding and intelligent agent tasks while maintaining strong general intelligence. With only 10 billion active parameters, MiniMax-M2 delivers the complex end-to-end tool usage performance expected from today's leading models, but in a more streamlined form factor, making deployment and scaling easier than ever before.