MiniMax M2.7

Chat Completions

minimax-m2.7

Minimax|Created Mar 18, 2026|204.8k context

Chat Completions

MiniMax-M2.7 is a next-generation large language model built for autonomous, real-world productivity and continuous improvement. Designed to take an active role in its own development, M2.7 incorporates advanced agent capabilities through multi-agent collaboration, allowing it to plan, execute, and improve complex tasks across dynamic environments. Trained for production-level performance, M2.7 supports workflows such as live debugging, root cause analysis, financial modeling, and full document creation across Word, Excel, and PowerPoint. It delivers strong benchmark results, including 56.2% on SWE-Pro and 57.0% on Terminal Bench 2, while reaching 1495 ELO on GDPval-AA, setting a new benchmark for multi-agent systems in real-world digital workflows.

Overview Specifications Activity Performance Uptime Examples

Pricing-50%

Pay-as-you-go rates for this model. More details can be found here.

Input Tokens (1M)

$0.15

Cached Input Tokens (1M)

$0.01

Output Tokens (1M)

$0.60

Capabilities

Input Modalities

Text

Output Modalities

Text

Supported Parameters

Available parameters for API requests

Max Completion TokensReasoning EffortResponse FormatStopTemperatureTool ChoiceToolsTop P

Usage Analytics

Token usage of this model on our platform

Throughput

Time-To-First-Token (TTFT)

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-m2.7",
    messages=[
        {"role": "user", "content": "What's 2+2?"}
    ],
    temperature=0.2,
)
print(resp.choices[0].message.content)