The AI landscape just welcomed a new heavyweight contender. On Day 1 of its five-day MiniMaxWeek event, Chinese AI company MiniMaxAI unveiled its flagship open-source reasoning model — MiniMax-M1 — along with a powerful, multi-functional MiniMax Agent. With a technical foundation built to rival models like OpenAI's GPT-4o, Anthropic’s Claude 4, and DeepSeek-R1, the MiniMax-M1 sets a bold precedent for large-scale, efficient, and transparent AI development in China and globally.
In this article, we’ll walk through the MiniMax-M1’s core architecture, benchmark performance, and how to test it yourself. We’ll also showcase the capabilities of the MiniMax Agent in real-world tasks — from app building to dynamic simulations.
What is MiniMax-M1?
MiniMax-M1 is a large-scale, open-source reasoning model developed by Shanghai-based AI firm MiniMaxAI. The model is designed around a hybrid attention mechanism and integrates Mixture-of-Experts (MoE) architecture to improve efficiency and performance. Notably, it supports multimodal input, handling text, images, web search queries, and even presentation files.
Released under the Apache 2.0 license, MiniMax-M1 is not only powerful — it’s fully open for community use and enterprise adoption, standing in contrast to many closed-source LLMs dominating the market.
Key Features
⚙️ Hybrid Attention + MoE Efficiency
MiniMax-M1 combines MoE with Lightning Attention, a technique that reduces inference costs dramatically. Compared to DeepSeek-R1, it achieves the same 100,000-token generation using just 25% of the FLOPs.
🧠 Massive Context Window
With a 1 million token input context window and support for up to 80,000 output tokens, MiniMax-M1 is on par with industry giants like Google’s Gemini 2.5 Pro. This allows it to digest long documents, complex codebases, and dense reasoning chains.
🧾 Two Variants – M1-40k and M1-80k
The model is available in:
- M1-40k: Mid-tier model for standard reasoning tasks.
- M1-80k: Advanced version optimized for agentic behavior and extended context.
🤖 Efficient Training at Scale
MiniMax trained the model using a custom RL algorithm called CISPO (Clipped Importance Sampling Policy Optimization). Training involved 512 A800 GPUs over 3 weeks, costing around $534,700 — a fraction of what competitors like OpenAI or Google reportedly spend.
📊 Benchmarks and Performance
MiniMax-M1 excels in:
- OpenAI-MRCR and LongBench-v2 for long-context reasoning
- TAU-bench for agentic task solving
MiniMax-M1: Benchmark Performance
| Benchmark | MiniMax-M1-80k | DeepSeek-R1 | GPT-4o | Claude 4 |
|---|---|---|---|---|
| LongBench-v2 | ✅ Best-in-class | ✅ | ✅ | ❌ |
| OpenAI-MRCR | ✅ | ❌ | ✅ | ✅ |
| TAU-bench | 🥇 Leader | ❌ | ✅ | ✅ |
The MiniMax-M1-80k particularly shines in agentic tasks, making it highly capable for workflow automation and tool integration.
How to Access MiniMax-M1
MiniMax-M1 can be accessed via:
- GitHub: github.com/minimaxai/m1 (Demo and inference scripts available)
- MiniMaxAI Platform: Cloud-hosted playground with model variants, API access, and long-context support.
- Docker Package: For local deployment and fine-tuning.
The team has also released a HuggingFace-compatible checkpoint for easy integration into existing LLM workflows.
MiniMax-M1: Hands-on Testing
We put the M1-80k through its paces on three different tasks:
🧪 Task 1: Animated Simulation
Prompt: “Simulate the physics of a bouncing ball inside a cube, considering friction and gravity.”
Result: MiniMax-M1 generated a Python script using Matplotlib + VPython for real-time 3D simulation, including adjustable parameters for gravity and bounce coefficient. Execution was successful, and animation was smooth and accurate.
🔍 Task 2: Web Search
Prompt: “Find the latest research on transformer quantization in June 2025.”
Result: The model accessed live web content (via its integrated search tool), summarized three recent papers, and generated citations with DOI links. The search and summary were accurate and context-aware.
🧩 Task 3: Logical Puzzle
Prompt: “If Alice is taller than Bob, and Bob is taller than Carol, who is the shortest?”
Result: Correctly reasoned that Carol is the shortest, and explained its deduction chain in plain language — no hallucinations or ambiguity.
MiniMax Agent in Action
Alongside the M1 model, MiniMaxAI unveiled the MiniMax Agent — a beta-stage autonomous AI assistant capable of real-world productivity tasks.
Key Capabilities:
- 🧱 Code Execution & App Building- Drag-and-drop components or use natural language to generate Python/JavaScript apps. The agent can build full-stack prototypes from scratch.
- 📊 Presentation Generation- From text prompts, the agent creates PowerPoint slides with layouts, charts, and talking points using design rules.
- 🌐 Web Automation & Research- Automates web browsing, fills out forms, extracts data, and synthesizes findings across multiple sources.
- 💼 Agentic Workflows- Integrates with APIs and external tools (like Google Sheets, Slack, and Notion) to complete tasks autonomously.
🧠 Example Use Case: We prompted the agent to “create a budget planner app with income/expense categories and monthly projections.”
Within 90 seconds, it scaffolded a working Streamlit app, auto-deployed it, and sent a shareable URL.
With MiniMax-M1 and the MiniMax Agent, MiniMaxAI has made a bold statement — China is ready to lead in open-source, high-performance AI. The combination of scalable reasoning, multimodal support, and powerful agentic capabilities make this release one of the most important of 2025 so far.
Whether you're a researcher, developer, or tech enthusiast, MiniMax-M1 is well worth exploring — not just for its performance, but for what it signals: a new chapter in open, efficient, and intelligent AI systems.
Have thoughts or questions about MiniMax-M1? Drop them in the comments or share your own use cases — let’s explore this powerful new tool together.