MiniMax: MiniMax M2-her
minimax/minimax-m2-her
Access MiniMax M2-her from MiniMax using Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "minimax/minimax-m2-her"
}).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-m2-her"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="minimax/minimax-m2-her",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "minimax/minimax-m2-her",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
MiniMax M2-her is a dialogue-first large language model built for immersive roleplay, character-driven chat, and expressive multi-turn conversations. It stays consistent in tone and personality across conversations and supports rich message roles to learn from example dialogue. This makes it well-suited for storytelling, AI companions, and conversational experiences where natural flow matters.
Context Window 66K
tokens
Max Output 2K
tokens
Input Cost $0.3
per million tokens
Output Cost $1.2
per million tokens
Release Date Jan 27, 2026
Output Speed 74
tokens / sec
Latency 1.14s
time to first token
Model Playground
Try MiniMax M2-her instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How MiniMax M2-her performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 77.7% |
| Humanity's Last Exam Cross-domain reasoning | 12.5% |
| LiveCodeBench Recent coding problems | 82.6% |
| SciCode Scientific programming | 36.1% |
| AIME 2025 Advanced math exam | 78.3% |
| IFBench Instruction following | 72.3% |
| LCR Long-context reasoning | 61.0% |
| Terminal-Bench Hard Agentic terminal tasks | 25.8% |
| τ²-Bench Tool use / agents | 86.8% |
Scores sourced from Artificial Analysis.
Find other MiniMax models →
MiniMax 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 M2.5
MiniMax M2.5 is a 230B-parameter Mixture-of-Experts model (10B active) from Shanghai-based MiniMax, designed for real-world productivity with state-of-the-art performance in coding (80.2% SWE-Bench Verified), agentic tool use, and search tasks. It rivals top models from Anthropic and OpenAI while costing 1/10th to 1/20th the price, positioning itself as frontier intelligence 'too cheap to meter.' The model excels at full-stack development, office work (Word, Excel, PowerPoint), and autonomous agent workflows.
ChatMiniMax M2.1
MiniMax-M2.1 is an enhanced version of M2 with significantly improved multi-language programming capabilities and office scenario support. It features more concise responses, better instruction following, and matches or exceeds Claude Sonnet 4.5 on coding benchmarks while maintaining excellent agent/tool scaffolding generalization.
Frequently Asked Questions
You can access MiniMax M2-her 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.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add MiniMax M2-her to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.3 |
| Output | $1.2 |
MiniMax M2-her was created by MiniMax and released on Jan 27, 2026.
MiniMax M2-her supports a context window of 66K tokens. For reference, that is roughly equivalent to 131 pages of text.
MiniMax M2-her can generate up to 2K tokens in a single response.
MiniMax M2-her scores 36.1 on the Artificial Analysis Intelligence Index, outperforming 80% of tracked models. On coding, it scores 29.2 (outperforms 69% of models). On math, it scores 78.3 (outperforms 72% of models).
Yes — the MiniMax M2-her 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.
Get started with Puter.js
Add MiniMax M2-her to your app without worrying about API keys or setup.
Read the Docs View Tutorials