MiniMax M2

About MiniMax M2

MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning, tool use, and multi-step task execution while maintaining low latency and deployment efficiency. The model excels in code generation, multi-file editing, compile-run-fix loops, and test-validated repair, showing strong results on SWE-Bench Verified, Multi-SWE-Bench, and Terminal-Bench. It also performs competitively in agentic evaluations such as BrowseComp and GAIA, effectively handling long-horizon planning, retrieval, and recovery from execution errors. Benchmarked by [Artificial Analysis](https://artificialanalysis.ai/models/minimax-m2), MiniMax-M2 ranks among the top open-source models for composite intelligence, spanning mathematics, science, and instruction-following. Its small activation footprint enables fast inference, high concurrency, and improved unit economics, making it well-suited for large-scale agents, developer assistants, and reasoning-driven applications that require responsiveness and cost efficiency. To avoid degrading this model's performance, MiniMax highly recommends preserving reasoning between turns. Learn more about using reasoning_details to pass back reasoning in our [docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#preserving-reasoning-blocks).

Model Specifications

💰 Real-World Cost Examples

Estimated monthly costs for common use cases

MiniMax Model Lineup

Compare all models from MiniMax to find the best fit

Similar Models from Other Providers

Cross-brand alternatives with similar capabilities

Llama 3 70B Instruct

Meta

Input: $0.51

Output: $0.74

Context: 8k

Weaver (alpha)

Mancer

Input: $0.50

Output: $0.75

Context: 8k

DeepSeek V3.1

DeepSeek

Input: $0.30

Output: $0.95

Context: 164k

Nex-N2-Pro

Nex Agi

Input: $0.25

Output: $1.00

Context: 262k

💡 Cheaper Alternatives

Same Brand (MiniMax)

Cross Brand

🚀 Quick Start

Get started with MiniMax M2 API

from openai import OpenAI

client = OpenAI(
    base_url="https://api.provider.com/v1",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="minimax/minimax-m2",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)
print(response.choices[0].message.content)

Continue Your Decision