MiniMax M2.5

Chat Completions

minimax-m2.5

Minimax|Created Feb 12, 2026|204.8k context

Chat Completions

MiniMax-M2.5 is a state-of-the-art large language model built for real-world productivity. Trained across a wide variety of complex digital work environments, M2.5 extends the coding strengths of M2.1 into broader office tasks—becoming fluent in creating and manipulating Word, Excel, and PowerPoint files, seamlessly switching context between different software tools, and collaborating across mixed agent and human teams. It scores 80.2% on SWE-Bench Verified, 51.3% on Multi-SWE-Bench, and 76.3% on BrowseComp. Compared to earlier generations, M2.5 is also more token-efficient, having been trained to plan effectively in order to optimize both its actions and outputs.

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 PFrequency PenaltyLogit BiasLogprobsPresence PenaltyParallel Tool CallsPrediction

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-m2.5",
    messages=[
        {"role": "user", "content": "What's 2+2?"}
    ],
    temperature=0.2,
)
print(resp.choices[0].message.content)