MiniMax M2 & Agent: Complete Guide to Chinese AI Platform

MiniMax M2, released October 27, 2025, delivers GPT-5-level coding performance at 8% of the cost with open-source weights. Learn about China's breakthrough AI model for agents, coding, and multimodal applications with the MiniMax Agent platform.

Key Takeaways

• Open-Source Powerhouse: MiniMax M2 achieves 69.4 on SWE-bench Verified, rivaling GPT-5's performance with open-sourced weights on Hugging Face
• Exceptional Cost Advantage: 92% cheaper than Claude with 2x inference speed at ~100 tokens/second. Input: $0.30/M tokens, Output: $1.20/M tokens
• Agent-First Architecture: Native support for Shell, Browser, Python interpreter, and MCP tools with stable long-chain tool-calling capabilities
• Multimodal Platform: MiniMax Agent handles text, video, audio, and image processing with expert-level multi-step planning and task execution
• Production Ready: Deploy via cloud API, self-host with vLLM/SGLang, or integrate with Claude Code, Cursor, and other development tools

Introduction

On October 27, 2025, Chinese AI company MiniMax released MiniMax M2, an open-source language model that achieves 69.4 on SWE-bench Verified—putting it within striking distance of GPT-5's 74.9 score. What makes this launch remarkable isn't just the performance: M2 costs 92% less than Claude Sonnet 4.5 while delivering 2x faster inference speeds.

MiniMax M2 isn't another general-purpose LLM trying to do everything. It's purpose-built for AI agents and coding workflows, with native support for Shell, Browser, Python interpreter, and Model Context Protocol (MCP) tools. Combined with the MiniMax Agent platform (launched June 2025), developers now have an end-to-end solution for building production AI agents at a fraction of the cost of Western alternatives.

This guide covers MiniMax M2's architecture, performance benchmarks, pricing, deployment options, and how it integrates with the MiniMax Agent platform to deliver multimodal AI capabilities for real-world applications.

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:

Native Tool Support:

• Shell: Execute bash commands and scripts
• Browser: Web automation and research
• Python Interpreter: Run Python code in isolated environments
• MCP (Model Context Protocol): Connect to GitHub, Slack, Figma, and other tools

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

Why This Matters: Open-source frontier models like M2 democratize access to state-of-the-art AI capabilities. Companies can deploy cutting-edge agents without vendor lock-in or concerns about API pricing changes.

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:

Agentic Performance Scores:

• τ²-Bench: 77.2 (tool use and task completion)
• BrowseComp: 44.0 (web research and navigation)
• FinSearchComp-global: 65.5 (financial research)
• ArtifactsBench: 66.8 (above Claude Sonnet 4.5 and DeepSeek-V3.2)

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-5: ~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

Bottom Line: MiniMax M2 delivers 90-95% of GPT-5's coding capabilities at 8% of the cost with 2x the speed. For production AI agents that process millions of tokens, these economics are game-changing.

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

MiniMax M2 API Costs:

• Input Tokens: $0.30 per million tokens (¥2.1 RMB)
• Output Tokens: $1.20 per million tokens (¥8.4 RMB)
• Cost vs Claude Sonnet 4.5: 8% of the price
• Cost Reduction: 92% cheaper per token

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:

1. Cloud API (Recommended for Most)

• Instant access via agent.minimax.io
• No infrastructure management required
• Automatic scaling and load balancing
• 99.9% uptime SLA

2. Self-Hosted with vLLM

# 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

3. Self-Hosted with SGLang

# Install SGLang
pip install "sglang[all]"

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

4. Integration with Development Tools

M2 integrates seamlessly with popular AI coding assistants:

• Claude Code: Use M2 as a drop-in replacement for Claude models
• Cursor: Configure as custom model endpoint
• Cline: Full agent workflow support
• Kilo Code: Native integration
• Droid: Mobile development agent support

Recommended Inference Parameters

For optimal performance, MiniMax recommends these sampling parameters:

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

Pro Tip: Self-hosting M2 on 4x A100 GPUs costs approximately $3-4/hour on cloud providers. At this rate, you'd need to process 13-17 million tokens per hour to break even with API pricing. For most use cases, the cloud API is more cost-effective.

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:

Agent Platform Features:

• Expert-Level Planning: Multi-step task decomposition and sequencing
• Flexible Execution: Adaptive strategies based on task requirements
• Multimodal Input: Text, video, audio, and image understanding
• Multimodal Generation: Create images, audio, and video content
• End-to-End Solutions: Complete task execution from planning to validation

Three Design Pillars

1. Programming Excellence

The agent handles complex logic, end-to-end testing simulation, and UX/UI optimization. Example capabilities:

• Generate full-stack applications from requirements
• Debug existing codebases with context awareness
• Optimize performance bottlenecks
• Create interactive animations and UI components

2. Multimodal Understanding & Generation

Process and create content across modalities:

• Analyze long-form video content and extract insights
• Generate 15-minute educational overviews with audio narration
• Create interactive tutorials with voiceover
• Build visual content from text descriptions

3. MCP Integration

Native support for Model Context Protocol enables connections to:

• GitHub/GitLab: Repository management, PR creation, CI/CD triggers
• Slack: Team communication and notifications
• Figma: Design collaboration and asset generation
• Custom Tools: Extend with your own MCP servers

Operational Modes

The platform offers two modes optimized for different use cases:

Lightning Mode:

• Best for: Fast Q&A, lightweight tasks, quick iterations
• Speed: Sub-second responses
• Use Cases: Code completion, simple queries, rapid prototyping

Pro Mode:

• Best for: Complex research, full-stack development, content creation
• Capabilities: Multi-step planning, tool orchestration, quality validation
• Use Cases: Building complete applications, comprehensive research, multimodal content

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

Example: Interactive Product Pages

The MiniMax Agent built a complete online Louvre museum experience in 3 minutes:

• Responsive layout with image galleries
• Interactive navigation and animations
• Artwork descriptions and historical context
• Mobile-optimized user experience

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)

Step 1: Sign up at agent.minimax.io and get your API key.

Step 2: Install the Python SDK:

pip install minimax-sdk

Step 3: Make your first API call:

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:

{
  "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

Pro Tip: Start with the cloud API during evaluation. Only consider self-hosting if you're processing 10M+ tokens per day or have strict data residency requirements.

MiniMax M2 vs Claude vs GPT-5

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

Performance Comparison

SWE-bench Verified Scores:

• Claude Sonnet 4.5: ~77.2 (best performance)
• GPT-5 (thinking): 74.9
• MiniMax M2: 69.4 (92% of GPT-5's score)
• DeepSeek-V3.2: Similar to M2

Cost Comparison (per 1M tokens)

Model	Input	Output	Relative Cost
MiniMax M2	$0.30	$1.20	1x (baseline)
Claude Sonnet 4.5	$3.00	$15.00	12.5x more expensive
GPT-5	$2.50	$10.00	7x more expensive

Speed Comparison

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

When to Choose Each Model

Choose MiniMax M2 if:

• Cost is a primary concern (agent workflows with high token volume)
• You need fast inference for interactive applications
• Open-source deployment is required (data privacy, self-hosting)
• Agent-first architecture is important (stable tool-calling)
• You're comfortable with 90-95% of frontier performance

Choose Claude Sonnet 4.5 if:

• You need absolute best coding performance (77.2 SWE-bench)
• Budget constraints are less critical
• Cloud API with strong safety guarantees is preferred
• You want proven enterprise support and reliability

Choose GPT-5 if:

• You need extended thinking and reasoning capabilities
• Complex multi-step problem solving is critical
• Budget allows for premium pricing

Recommendation: For most production AI agent use cases, MiniMax M2's combination of performance (69.4 SWE-bench), speed (100 tok/s), and cost ($0.30/$1.20 per 1M tokens) makes it the most economically viable choice. Reserve Claude/GPT for critical workflows where the extra 5-10% performance justifies 10-20x higher costs.

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

What is MiniMax M2 and when was it released?

MiniMax M2 is a 230B parameter Mixture-of-Experts (MoE) AI model with 10B active parameters per token, released on October 27, 2025. It's designed specifically for agentic AI applications with native tool-calling capabilities. The model achieved 69.4 on the SWE-bench Verified benchmark (92% of GPT-5's performance) and is available both via cloud API and as open-source weights on Hugging Face.

How much does MiniMax M2 cost compared to Claude and GPT?

MiniMax M2 is dramatically cheaper than frontier models: $0.30 per million input tokens and $1.20 per million output tokens. This is 92% cheaper than Claude Sonnet 4.5 ($3/$15) and significantly less expensive than GPT-5 ($2.50/$10). For example, a 10M token agentic workflow costs $15 with M2 versus $180 with Claude—a 12x cost reduction. The model also runs at ~100 tokens/second, approximately 2x faster than Claude.

Can I self-host MiniMax M2 on my own infrastructure?

Yes, MiniMax M2 is fully open-source with weights available on Hugging Face under a permissive commercial license. You can deploy it using vLLM (recommended for production), SGLang (optimized for agentic workloads with structured outputs), or TGI (Hugging Face's inference server). The 230B parameter model requires at least 4x H100 80GB GPUs for efficient inference. Self-hosting eliminates API costs and enables complete data privacy.

What is the MiniMax Agent platform?

MiniMax Agent is a multimodal AI agent platform launched June 19, 2025, that complements the M2 model. It provides a unified interface for text, video, audio, and image processing with native tool integration. The platform supports Model Context Protocol (MCP) for standardized tool access, includes prebuilt integrations for web search, data analysis, and file systems, and offers multi-step task planning with automatic error recovery. It's accessible via agent.minimax.io with a free trial through November 7, 2025.

How does MiniMax M2 compare to Claude Sonnet 4.5 and GPT-5?

MiniMax M2 (69.4 SWE-bench) achieves 90% of Claude Sonnet 4.5's performance (77.2) and 94% of GPT-5's (74.9) at 8% of their cost. Claude leads in reasoning and coding quality but costs 12x more. GPT-5 excels at complex reasoning but costs 7x more. M2 is ideal for high-volume agent workflows where 90-95% frontier performance is acceptable, especially for tasks like code review, documentation, testing, and data transformation where cost and speed matter more than marginal quality improvements.

What are the best use cases for MiniMax M2?

MiniMax M2 excels at: (1) High-volume agent workflows processing thousands of tasks per day where cost savings scale dramatically, (2) Self-hosted deployments requiring data privacy or regulatory compliance, (3) DevOps automation for CI/CD pipelines, log analysis, and infrastructure monitoring, (4) Research and experimentation enabling rapid prototyping without expensive API bills, and (5) Code intelligence tasks like review, refactoring, documentation, and testing where good-enough quality at low cost is more valuable than perfect accuracy.

Does MiniMax M2 integrate with existing development tools?

Yes, MiniMax M2 integrates with popular development environments through OpenAI-compatible API endpoints. It works with Claude Code (via custom model configuration), Cursor IDE (as custom model), Continue.dev, Cline, and other tools supporting OpenAI API format. The model supports function calling/tool use following OpenAI's format, making it a drop-in replacement for workflows originally designed for GPT models.

What are MiniMax M2's limitations?

MiniMax M2 has several important limitations: (1) Benchmark gap—it scores 7.8 points below Claude Sonnet 4.5 on SWE-bench, which may matter for complex reasoning tasks, (2) Limited documentation with most resources in Chinese, requiring translation, (3) Smaller ecosystem compared to OpenAI/Anthropic with fewer community tools and examples, (4) Self-hosting complexity requiring significant GPU resources (4x H100 80GB minimum), and (5) Unknown reliability in production at scale compared to battle-tested providers like OpenAI and Anthropic.

How do I get started with MiniMax M2?

For cloud API: Sign up at agent.minimax.io, obtain your API key, and use the free trial available through November 7, 2025. The API is OpenAI-compatible, so you can swap in the MiniMax endpoint with minimal code changes. For self-hosting: Pull the model from Hugging Face (MiniMax-AI/MiniMax-M2), deploy using vLLM or SGLang on GPU infrastructure (minimum 4x H100 80GB), and configure your application to use the local endpoint. Start with small-scale tests to validate performance before scaling up.

What is the difference between MiniMax M2 and MiniMax Agent?

MiniMax M2 is the underlying AI model—a 230B parameter MoE architecture optimized for coding and tool use. MiniMax Agent is the platform layer built on top of M2 that provides multimodal capabilities (text, video, audio, images), orchestration for multi-step tasks, standardized tool integration via Model Context Protocol (MCP), and pre-built connectors for common services. Think of M2 as the engine and Agent as the complete vehicle with controls, sensors, and interfaces for practical applications.