Fastest Long Reasoning, Longest Context AI Model Yet
Explore the frontier of long-context AI reasoning with **MiniMax M1**, the cutting-edge model built for memory-intensive and code-first scenarios.
MiniMax M1: Fastest Long Reasoning, Longest Context AI Model Yet
Published on MiniMaxM.com
Explore the frontier of long-context AI reasoning with MiniMax M1, the cutting-edge model built for memory-intensive and code-first scenarios.
Introduction
MiniMax M1, developed by the innovative AI team behind https://www.minimaxm.com, is the latest evolution in open-weight LLMs. It stands out as the fastest long-context reasoning model in its class — boasting a staggering 1 million token input context, roughly equivalent to 10 full-length novels.
Unlike traditional LLMs that struggle past 16K tokens, MiniMax M1 is purpose-built to retain, reason, and generate across massive context windows without compromising on performance. And yes — it’s open-weight and available now.
Why MiniMax M1 Exists
The name might sound modest, but the mission is bold: scale with efficiency, not waste. Here’s what makes MiniMax M1 exceptional:
- 456B parameter hybrid architecture
- Mixture-of-Experts (MoE) design — only selective parts of the model are activated per token
- Lightning Attention, a smarter, leaner self-attention mechanism
- Up to 75% FLOPs savings compared to DeepSeek R1 on long generations
- Trained in real software sandbox environments, math-heavy tasks, and agent-like behaviors using CISPO (Clipped Importance Sampling for Policy Optimization) — making reinforcement learning stable in hybrid models
MiniMax M1 is engineered not only for capability, but for sustainable performance at scale.
Benchmarks: Punching Above Its Weight
For those who value real-world task performance, MiniMax M1 (especially the 80K variant) delivers:
- SWE-bench (Software Engineering): 56.0
Beating Qwen3–235B and DeepSeek-R1
- LiveCodeBench: 65%
Solid performance in code generation tasks
- TAU-bench Retail: 62.8
Matches Claude-4 in select agent-style evaluations
- OpenAI MRCR (long-context): Strong performance
MiniMax M1–80K is among the few open models that even show up in this arena
It’s not perfect everywhere — factual QA scores trail behind models like Claude and Gemini — but for software, logic-heavy, and tool-use scenarios, MiniMax M1 excels.
1 Million Tokens: No Compression Tricks
Most models hallucinate summaries past 16K. Not MiniMax M1.
At MiniMaxM.com, we’ve verified and validated that MiniMax M1 truly reasons through full million-token sequences — without compression, hacks, or approximations.
Imagine feeding it:
- A year’s worth of Jira tickets
- Slack messages from your dev team
- Git diffs and inline comments
And MiniMax M1 could still trace back a regression bug from Q4 — that’s the scale and depth we’re talking about.
Choose Your Version
MiniMax M1 is available in two configurations:
- MiniMax M1–80K: For professionals, agents, code workflows — high recall and reasoning depth
- MiniMax M1–40K: Lighter model, useful for general tasks but with limited agentic reasoning
Recommended deployment: vLLM or similar lightweight serving systems — you don’t want your infrastructure sweating bullets for long-context tasks.
Tool Support & Prompt Recipes
Out of the box, MiniMax M1 supports:
- Function calling
- Multimodal API stack: Image, video, and voice understanding
- Chat & agent frameworks
And yes — we provide prompt engineering recipes to make the most of its capabilities:
-
Math tasks:
"Please reason step by step, and put your final answer within {}." -
Web development:
Native understanding of HTML, CSS, and JavaScript -
Generic tasks:
A basic system prompt performs well without fine-tuning
Should You Try MiniMax M1?
For hobbyists: Possibly overkill unless you’re tinkering with massive datasets.
For builders, agent developers, AI toolchain engineers:
Absolutely. If you're tired of being throttled by API limits or hallucinations, MiniMax M1 offers freedom + performance.
- Free from OpenAI’s shifting rate tiers
- More stable than Gemini for reasoning
- More transparent than Anthropic’s models
And unlike generalist models that claim to do everything, MiniMax M1 focuses on what it does best — reasoning, remembering, and executing on long, structured input.
Try MiniMax M1 for Free
The weights are open-source and available now.
Head over to https://www.minimaxm.com to start testing, integrating, or benchmarking the model for your own use cases.
MiniMax M1 is not just an LLM — it’s a long-context reasoning engine.
Dive into the future of memory-intensive AI at https://www.minimaxm.com
Stay updated: More developer tools, fine-tuned versions, and tutorials coming soon at MiniMaxM.com.