MiniMax: MiniMax M1
minimax/minimax-m1
Try MiniMax M1 for free in your browser, and add it to your app for free with Puter.js AI API.
Try it free Add to your app// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "minimax/minimax-m1"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "minimax/minimax-m1"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
MiniMax-M1 is the world's first open-source hybrid-attention reasoning model, featuring a 1 million token context window and 80K reasoning output budget. It excels in software engineering, long-context tasks, and complex reasoning while being trained with an efficient CISPO reinforcement learning algorithm.
Context Window 1M
tokens
Max Output 40K
tokens
Input Cost $0.55
per million tokens
Output Cost $2.2
per million tokens
Input text
modalities
Tool Use Yes
Release Date Jun 17, 2025
Try MiniMax M1 for free
Try MiniMax M1 instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How MiniMax M1 performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 69.7% |
| Humanity's Last Exam Cross-domain reasoning | 8.9% |
| LiveCodeBench Recent coding problems | 71.1% |
| MATH-500 Competition math | 98.0% |
| AIME 2024 Advanced math exam | 84.7% |
| AIME 2025 Advanced math exam | 61.0% |
| IFBench Instruction following | 41.8% |
| LCR Long-context reasoning | 57.7% |
| Terminal-Bench Hard Agentic terminal tasks | 3.0% |
| τ²-Bench Tool use / agents | 34.2% |
Scores sourced from Artificial Analysis.
Find other MiniMax models →
MiniMax M3
MiniMax M3 is a frontier-level multimodal language model built for long-horizon coding, agentic workflows, and complex reasoning. It introduces MiniMax Sparse Attention (MSA), which delivers 15x faster decoding and 9x faster prefill at 1M-token context compared to the prior generation. M3 scores 59.0% on SWE-Bench Pro — surpassing GPT-5.5 and Gemini 3.1 Pro — and achieves the highest score on Claw-Eval (74.5%) for autonomous agent tasks. It accepts text, image, and video inputs natively and supports tool calling through standard MCP scaffolding. Ideal for developers building coding agents, long-document pipelines, and multi-step automation that require sustained, multi-hour autonomous execution.
ChatMiniMax M2.7
MiniMax M2.7 is a proprietary reasoning LLM from Chinese AI startup MiniMax, released on March 18, 2026, notable for being one of the first commercial models to actively participate in its own training through autonomous self-evolution loops. It excels at agentic coding workflows with a 56.2% score on SWE-Pro and strong performance in office productivity tasks, scoring the highest ELO (1495) on GDPval-AA among open-source-tier models. It targets developers building complex agent systems and automated workflows.
ChatMiniMax M2.7 Highspeed
MiniMax M2.7 Highspeed is a high-throughput, inference-optimized variant of MiniMax M2.7, delivering approximately 100 tokens per second — roughly 66% faster than the standard version. It shares the same model weights and MoE architecture as M2.7, so output quality and reasoning capability are identical; the speed advantage comes entirely from inference-layer routing and batching optimizations. It supports text and image inputs with a 204K context window and features automatic prompt caching and parallel tool calling. Best suited for live coding assistants, autonomous agent pipelines, and interactive workflows where low latency and high throughput matter.
Frequently Asked Questions
You can access MiniMax M1 by MiniMax through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
MiniMax M1 is free to try with a Puter account. Every account includes a free AI allowance, and you can chat with it in the playground on this page. You can upgrade your account anytime for a larger allowance.
MiniMax M1 is free to integrate using the Puter.js AI API. With the User-Pays Model, you can add AI to your app for $0, since users cover their own AI usage through their Puter account.
| Price per 1M tokens | |
|---|---|
| Input | $0.55 |
| Output | $2.2 |
MiniMax M1 was created by MiniMax and released on Jun 17, 2025.
MiniMax M1 supports a context window of 1M tokens. For reference, that is roughly equivalent to 2,000 pages of text.
MiniMax M1 can generate up to 40K tokens in a single response.
MiniMax M1 accepts the following input types: text. It produces: text.
Yes, MiniMax M1 supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
MiniMax M1 scores 11.7 on the Artificial Analysis Intelligence Index, outperforming 47% of tracked models. On math, it scores 61.0 (outperforms 56% of models).
Yes — the MiniMax M1 API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Add MiniMax M1 to your app for free
Developers can integrate MiniMax M1 for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.