MiniMax M2 & Agent: Complete Guide to Chinese AI Platform

What is MiniMax M2?

MiniMax M2 is a 230 billion parameter language model with 10 billion active parameters, optimized specifically for AI agent workflows and coding tasks. Released on October 27, 2025, it represents a new generation of Chinese AI models designed to compete directly with Western frontier models like Claude Sonnet 4.5 and GPT-5.

Core Architecture

M2 uses a mixture-of-experts (MoE) architecture with 230B total parameters but only 10B active at inference time. This design delivers several advantages:

• Inference Speed: ~100 tokens/second (approximately 2x faster than Claude Sonnet 4.5)
• Cost Efficiency: Smaller active parameter count reduces compute requirements dramatically
• Model Quality: Large total parameter pool enables specialized expertise across different task types
• Deployment Flexibility: Efficient enough to run on consumer hardware via vLLM or SGLang

Agent-First Design Philosophy

Unlike general-purpose LLMs that bolt on tool-calling as an afterthought, MiniMax M2 was built from the ground up for stable long-chain tool-calling. The model natively supports:

This agent-first approach means M2 can handle complex multi-step workflows that require calling multiple tools in sequence—a capability that ranks it in the top five globally on Artificial Analysis benchmarks across 10 different test sets.

Open Source Commitment

MiniMax open-sourced the M2 model weights on Hugging Face immediately upon release. This decision puts M2 in a rare category: frontier-level performance with complete transparency and self-hosting options. Developers can:

• Download weights and fine-tune for specific use cases
• Deploy on private infrastructure without API dependencies
• Audit model behavior and safety characteristics
• Build derivative models without licensing restrictions

Performance & Benchmarks

MiniMax M2 delivers competitive performance across coding, reasoning, and agentic benchmarks. Here's how it stacks up against leading models:

SWE-bench Verified: 69.4

On SWE-bench Verified, the gold-standard benchmark for real-world coding tasks, M2 scores 69.4. This places it:

• GPT-5 (thinking): 74.9 (5.5 points ahead)
• MiniMax M2: 69.4
• Claude Sonnet 4.5: ~77.2 (but at 12.5x the cost)
• DeepSeek-V3.2: Similar range

Importantly, M2 was tested using the claude-code CLI with 300 max steps, ensuring consistency with how these models perform in real development workflows—not just isolated benchmark scenarios.

Agentic Task Benchmarks

M2 excels at multi-step agentic workflows that require planning, tool use, and error recovery:

These scores place M2 "at or near the level of top proprietary systems like GPT-5 (thinking) and Claude Sonnet 4.5," according to independent analysis from Artificial Analysis.

Real-World Accuracy Testing

Independent testers ran blended accuracy tests (code unit tests, structured extraction correctness, and reasoning acceptability) with results:

• MiniMax M2: ~95% accuracy
• GPT-4o: ~90% accuracy
• Claude Sonnet 4.5: ~88-89% accuracy

While these results come from limited testing scenarios, they suggest M2's practical performance often exceeds what isolated benchmarks might predict.

Inference Speed Advantage

M2's efficient architecture delivers ~100 tokens per second inference speed—approximately double the speed of competing models like Claude Sonnet 4.5. For AI agents that generate thousands of tokens across multi-step workflows, this speed advantage directly translates to:

• Faster task completion times
• Lower compute costs per task
• Better user experience for interactive applications
• More iterations possible within budget constraints

Pricing & Deployment Options

MiniMax M2's pricing strategy makes frontier-level AI accessible to companies of all sizes. Here's the complete breakdown:

API Pricing

To put this in perspective: a typical AI agent workflow that processes 100K input tokens and generates 50K output tokens would cost:

• MiniMax M2: $0.09 per workflow
• Claude Sonnet 4.5: ~$1.05 per workflow
• GPT-5: ~$0.75 per workflow

For companies running thousands of agent workflows daily, M2's pricing enables use cases that would be economically infeasible with Western APIs.

Free Trial Period

MiniMax is offering an extended free trial through November 7, 2025 (UTC). This gives developers 11 days to:

• Test M2's performance on production workloads
• Compare against Claude, GPT-4, and other models
• Validate cost savings with real usage patterns
• Build proof-of-concept agents before committing to paid usage

Deployment Options

M2's open-source nature enables multiple deployment strategies:

# Install vLLM
pip install vllm

# Download MiniMax M2 weights
git clone https://huggingface.co/MiniMaxAI/MiniMax-M2

# Launch inference server
vllm serve MiniMaxAI/MiniMax-M2 \
  --trust-remote-code \
  --tensor-parallel-size 4 \
  --max-model-len 16384

# Install SGLang
pip install "sglang[all]"

# Launch with optimized settings
python -m sglang.launch_server \
  --model-path MiniMaxAI/MiniMax-M2 \
  --port 30000 \
  --tp 4

Recommended Inference Parameters

For optimal performance, MiniMax recommends these sampling parameters:

{
  "temperature": 1.0,
  "top_p": 0.95,
  "top_k": 20,
  "max_tokens": 4096
}

MiniMax Agent Platform

While MiniMax M2 provides the foundational model, the MiniMax Agent platform (launched June 19, 2025) delivers the complete infrastructure for building production AI agents. After nearly 60 days of internal testing—with over 50% of MiniMax's own team using it as a daily tool—the platform is battle-tested for real-world workloads.

Core Capabilities

MiniMax Agent is described as "a general intelligent agent designed to tackle long-horizon, complex tasks." It excels at:

Three Design Pillars

Operational Modes

The platform offers two modes optimized for different use cases:

Platform Architecture

MiniMax Agent currently relies on multiple specialized models rather than a single unified system. While this introduces "some overhead in cost and efficiency" (as acknowledged by the company), it enables best-in-class performance for each modality. The team is actively working on consolidation to improve affordability for everyday use.

Access the platform at agent.minimax.io (contact for enterprise pricing).

Use Cases & Applications

MiniMax M2 and the Agent platform excel at specific categories of tasks. Here are proven use cases with concrete examples:

1. Full-Stack Development

2. Educational Content Generation

The platform can generate comprehensive educational materials:

• 15-minute overview videos with professional narration
• Interactive tutorials with step-by-step voiceover
• Visual diagrams and concept explanations
• Quizzes and assessment materials

3. Code Review & Refactoring

M2's strong coding capabilities make it ideal for:

• Automated code review with contextual suggestions
• Large-scale refactoring across codebases
• Performance optimization recommendations
• Security vulnerability detection and fixes

4. Research & Analysis

Pro Mode excels at comprehensive research workflows:

• Multi-source research synthesis
• Competitive analysis reports
• Market research and trend identification
• Technical documentation analysis

5. Workflow Automation

With MCP integration, automate complex business processes:

• GitHub PR automation (review, testing, deployment)
• Slack-based team workflows and notifications
• Design-to-code pipelines with Figma integration
• Custom tool orchestration for domain-specific tasks

Getting Started with MiniMax M2

Here's how to start using MiniMax M2 in your projects today:

Option 1: Cloud API (Fastest Setup)

pip install minimax-sdk

import minimax

# Initialize client
client = minimax.Client(api_key="your-api-key")

# Generate completion
response = client.chat.completions.create(
    model="minimax-m2",
    messages=[
        {"role": "system", "content": "You are a helpful coding assistant."},
        {"role": "user", "content": "Write a Python function to validate email addresses."}
    ],
    temperature=1.0,
    top_p=0.95
)

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

Option 2: Self-Hosted Deployment

For complete control and data privacy, deploy M2 on your own infrastructure:

# Clone model weights from Hugging Face
git lfs install
git clone https://huggingface.co/MiniMaxAI/MiniMax-M2

# Install vLLM (recommended for production)
pip install vllm

# Launch inference server
vllm serve MiniMaxAI/MiniMax-M2 \
  --host 0.0.0.0 \
  --port 8000 \
  --tensor-parallel-size 4 \
  --max-model-len 16384 \
  --trust-remote-code

# Server runs at http://localhost:8000
# Use OpenAI-compatible API endpoints

Option 3: Integration with Claude Code

Use M2 as a drop-in replacement for Claude models:

# In your Claude Code config
{
  "model": "minimax-m2",
  "api_base": "https://agent.minimax.io/v1",
  "api_key": "your-api-key"
}

Testing During Free Trial

The free trial (through November 7, 2025) is perfect for evaluation. Run these tests:

• Code Generation: Compare M2 vs Claude/GPT on your typical coding tasks
• Agent Workflows: Build a simple agent with Shell, Browser, and Python tools
• Speed Testing: Measure tokens/second for your workloads
• Cost Analysis: Track token usage and calculate monthly costs
• Quality Assessment: Evaluate output quality on domain-specific tasks

MiniMax M2 vs Claude vs GPT-5

Here's how MiniMax M2 compares to Western frontier models across key dimensions:

Performance Comparison

Cost Comparison (per 1M tokens)

Speed Comparison

• MiniMax M2: ~100 tokens/second
• Claude Sonnet 4.5: ~50 tokens/second
• GPT-5: ~40 tokens/second

When to Choose Each Model

Conclusion

MiniMax M2 represents a significant milestone in the democratization of frontier AI capabilities. By delivering 69.4 on SWE-bench Verified at 8% of Claude's cost with double the inference speed, M2 makes production AI agents economically viable for companies that previously couldn't justify the expense.

The open-source release amplifies this impact: developers can now deploy cutting-edge agentic AI on private infrastructure without vendor lock-in or concerns about API pricing changes. Combined with the MiniMax Agent platform's multimodal capabilities and MCP integrations, teams have an end-to-end solution for building sophisticated AI workflows.

For organizations evaluating AI strategies in late 2025, MiniMax M2 should be on the shortlist—especially for use cases involving:

• High-volume agent workflows (thousands of tasks per day)
• Cost-sensitive applications where 90-95% frontier performance is sufficient
• Self-hosted deployments for data privacy or compliance
• Rapid iteration where 2x faster inference enables tighter feedback loops

The free trial through November 7, 2025 provides a risk-free opportunity to validate these claims with your own workloads. Start at agent.minimax.io and see if M2's performance-cost-speed tradeoff works for your use case.

Frequently Asked Questions