MiniMax M2 Open-Source AI Model | Generated by AI
What is MiniMax M2?
MiniMax M2 is a newly released open-source large language model (LLM) from MiniMax AI, a Chinese AI company focused on foundation models for text, audio, image, and video. Launched just hours ago (as of October 27, 2025), it’s a Mixture of Experts (MoE) model optimized for coding and agentic workflows—think autonomous AI agents that plan, execute tools, and handle complex tasks like full-stack development, data analysis, or multi-step research. It’s not a generalist like GPT or Claude but shines in end-to-end programming and agent applications, integrating seamlessly with tools like Claude Code, Cursor, or browser/shell interpreters.
Key specs:
- Parameters: 230 billion total, but only 10 billion active (for efficiency).
- Context Length: Up to 128K tokens.
- Deployment: Available on Hugging Face under MIT license; supports frameworks like SGLang and vLLM for self-hosting.
- Access: Free API trial until November 7, 2025, via MiniMax Agent (agent.minimax.io). Paid API is $0.30 per million input tokens and $1.20 per million output tokens—about 8% the cost of Claude 3.5 Sonnet.
It’s powered by an “interleaved thinking” approach (using <think> tags for reasoning), making it great for iterative tasks like debugging code or chaining tools (e.g., search + code execution + verification).
Is It Good?
Yes, it’s exceptionally good—especially if you’re into coding, agents, or cost-sensitive deployments. Early benchmarks position it as the top open-source model globally, edging out closed-source heavyweights in key areas while being 2x faster (~100 tokens per second) and far cheaper. Here’s a quick breakdown:
| Category | MiniMax M2 Score | Top Competitors | Notes |
|---|---|---|---|
| Overall Intelligence (Artificial Analysis composite) | 61 | Claude Sonnet 4.5: ~59, Grok 4 Fast: lower, Gemini 2.5 Pro: lower | #1 open-source; covers math, science, coding, agents. |
| Coding (SWE-bench Verified) | 69.4 | Claude 3.5 Sonnet: ~65, DeepSeek-V3: 68 | Excels in real-world repo editing and multi-step fixes. |
| Agentic Tasks (GAIA text-only) | 75.7 | Claude 3.5: 74, Gemini 1.5: 72 | Strong in tool-calling chains (browser, shell, Python). |
| Reasoning (MMLU-Pro) | 82 | GPT-4o: 81, GLM-4.6: 80 | Competitive on knowledge/math; leads in telecom-specific (87). |
| Speed/Cost | 2x Sonnet speed, 1/12th price | N/A | Ideal for high-throughput agents; lower latency for interactive use. |
User buzz on X is electric—folks are calling it a “game changer” and “crazy” for outperforming pricier models in dev stacks. One dev stack tip: Pair it with Cursor for a “perfect” free alternative to premium tools. That said, it’s brand new, so real-world testing (beyond benchmarks) is ongoing; a few early Reddit takes note it shines superficially but can falter on deep, custom coding. Overall, if agents or code are your jam, it’s a no-brainer to try—especially free right now.
MiniMax M2 Official Announcement
GitHub Repo & Benchmarks
Artificial Analysis Overview
Analytics Vidhya Review
Reddit Discussion