MiniMax M2

Chat Completions

minimax-m2

Minimax|Created Nov 4, 2025|196.6k context

Chat Completions

MiniMax-M2 is a compact, efficient language model with 10B active (230B total) parameters, optimized for coding and agentic workflows. It achieves near-frontier reasoning and tool use with low latency and deployment cost. The model excels in code generation, multi-file editing, compile-run-fix cycles, and automated test repair, showing strong results on SWE-Bench and Terminal-Bench. MiniMax-M2 performs well in agentic benchmarks like BrowseComp and GAIA, handling long-term planning, retrieval, and error recovery. With a small activation footprint, it delivers fast inference and high concurrency, making it ideal for developer tools, agents, and applications that demand cost-effective, responsive reasoning.

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)

$0.10

Output Tokens (1M)

$0.50

Capabilities

Input Modalities

Text

Output Modalities

Text

Supported Parameters

Available parameters for API requests

Frequency PenaltyLogit BiasLogprobsMax Completion TokensPresence PenaltyResponse FormatStopTemperatureTop PReasoning EffortToolsTool Choice

Usage Analytics

Token usage of this model on our platform

Throughput

Not enough throughput data

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