MiniMax M3

MiniMax's efficient long-context model — capable general reasoning and long-form writing at a very low price, with a 1M+ token context window.

POST/v1/ai/chat

$0.01/call

Overview

MiniMax's efficient long-context model — capable general reasoning and long-form writing at a very low price, with a 1M+ token context window.

Property	Value
Model ID	`minimax/minimax-m3`
Context Window	1,048,576 tokens
Max Output	32,768 tokens
Input Price	$0.30 / 1M tokens
Output Price	$1.20 / 1M tokens

Usage

const res = await fetch('https://api.yepapi.com/v1/ai/chat', {
  method: 'POST',
  headers: {
    'x-api-key': 'YOUR_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'minimax/minimax-m3',
    messages: [{ role: 'user', content: 'Write a technical blog post comparing approaches to building RAG systems.' }],
  }),
});
const { data } = await res.json();
console.log(data.message.content);

curl -X POST https://api.yepapi.com/v1/ai/chat \
  -H "x-api-key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "minimax/minimax-m3", "messages": [{"role": "user", "content": "Write a technical blog post comparing approaches to building RAG systems."}]}'

Request Body

Parameter	Type	Required	Description	Default
`model`	`string`	Yes	Model ID (e.g. `minimax/minimax-m3`)	—
`messages`	`Message[]`	Yes	Array of `{ role, content }` objects	—
`maxTokens`	`number`	No	Maximum tokens in the response	Model default
`temperature`	`number`	No	Sampling temperature (0.0–2.0)	`1.0`
`topP`	`number`	No	Nucleus sampling threshold	`1.0`
`frequencyPenalty`	`number`	No	Penalize repeated tokens	`0`
`presencePenalty`	`number`	No	Penalize tokens already present	`0`
`stream`	`boolean`	No	Enable SSE streaming	`false`

Info

All AI models use the /v1/ai/chat endpoint. Specify the model with the model field.

Response

{
  "ok": true,
  "data": {
    "model": "minimax/minimax-m3",
    "message": {
      "role": "assistant",
      "content": "# Building RAG Systems: A Comparison of Approaches\n\nRetrieval-augmented generation spans a spectrum from naive top-k similarity search to hybrid retrieval with reranking and query rewriting. This post walks through chunking strategies, embedding choices, vector vs. keyword vs. hybrid retrieval, and when an agentic, multi-hop pipeline pays for its added latency."
    },
    "usage": {
      "promptTokens": 16,
      "completionTokens": 245,
      "totalTokens": 261
    }
  }
}

Streaming

Set "stream": true to receive Server-Sent Events. Each chunk contains a delta object:

data: {"delta":{"content":"#"},"model":"minimax/minimax-m3","index":0}
data: [DONE]

Under the Hood

We handle auth, billing, and response normalization — you just send messages.