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MiniMax M2 API: toman pricing and code

minimax/minimax-m2

Tool callingReasoning
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Input · per 1M tokens
88,286 toman
Output · per 1M tokens
353,144 toman
One 1,000-word request ≈
574 toman

You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups

MiniMax M2 example: tool calling

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

from openai import OpenAI
import json

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

tools = [{
    "type": "function",
    "function": {
        "name": "check_stock",
        "description": "Returns the stock level of a product",
        "parameters": {
            "type": "object",
            "properties": {"sku": {"type": "string"}},
            "required": ["sku"],
        },
    },
}]

messages = [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}]
first = client.chat.completions.create(model="minimax/minimax-m2", messages=messages, tools=tools)
call = first.choices[0].message.tool_calls[0]

# you run the function yourself — the model never touches the database
args = json.loads(call.function.arguments)
result = {"sku": args["sku"], "qty": 7}

messages += [first.choices[0].message, {"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)}]
final = client.chat.completions.create(model="minimax/minimax-m2", messages=messages, tools=tools)
print(final.choices[0].message.content)
import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });

const tools = [{
  type: "function",
  function: {
    name: "check_stock",
    description: "Returns the stock level of a product",
    parameters: { type: "object", properties: { sku: { type: "string" } }, required: ["sku"] },
  },
}];

const messages = [{ role: "user", content: "How many of the Nike NK-42 shoe are in stock?" }];
const first = await client.chat.completions.create({ model: "minimax/minimax-m2", messages, tools });
const call = first.choices[0].message.tool_calls[0];

const args = JSON.parse(call.function.arguments);
const result = { sku: args.sku, qty: 7 };

messages.push(first.choices[0].message, { role: "tool", tool_call_id: call.id, content: JSON.stringify(result) });
const final = await client.chat.completions.create({ model: "minimax/minimax-m2", messages, tools });
console.log(final.choices[0].message.content);
curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minimax/minimax-m2",
    "messages": [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "check_stock",
        "parameters": {"type": "object", "properties": {"sku": {"type": "string"}}, "required": ["sku"]}
      }
    }]
  }'

What is MiniMax M2 good for?

MiniMax M2 comes from MiniMax; in uttapen you reach it with the model id "minimax/minimax-m2". It accepts up to 204,800 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 131,072 tokens. On price it sits in the "cheap" band — cheaper than 248 and dearer than 150 of the other paid models in the catalogue.

Capabilities available on this id: it supports tool calling, so it can invoke your own functions with valid arguments; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.

For a back-of-envelope figure: about 574 toman per 1,000-word exchange (88,286 in, 353,144 out, per 1M tokens). You are always charged for the usage the request actually reported, never for the estimate, and a failed request costs nothing.

The closest alternative with the same capabilities from a different provider is DeepSeek V3, and MiniMax M2 costs about the same, and its context window is larger. Both run on the same key and the same code, so trying the other one is a single string change.

To make the figure concrete: 100,000 toman of credit buys roughly 174 thousand-word requests on MiniMax M2, and every 1,000 toman is about 1,742 words of round trip. A job with one million input tokens and one million output tokens comes to 441,430 toman in total. Filling this model's 204,800-token window costs 18,081 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "minimax/minimax-m1", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 2.2× cheaper (574 against 1,238 toman). The context windows differ too: 204,800 against 1,000,000 tokens. Maximum answer length differs as well: 131,072 against 40,000 tokens. On parameters the other takes frequency_penalty, presence_penalty, repetition_penalty, seed.

MiniMax has 9 models in our catalogue; the cheapest is MiniMax M3.1 Flash at 338 toman per thousand words and the dearest MiniMax M1 at 1,238. It does not support response_format, seed, structured_outputs, which most models here do, so test before switching if your code relies on them. By context size the nearest option from another provider is GLM 4.6 at 204,800 tokens.

What to use it for: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, agents that reach out to APIs and databases, analysing a long document or codebase in one request.

Provider's own description

MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning,...

Three real jobs, priced on this model

Each figure is derived from the prices above and moves when they do.

JobTokensCost
One chat turn with a medium history1,500 in + 400 out274 toman
Summarising a ten-page document4,000 in + 600 out565 toman
Classifying a thousand short rows120,000 in + 20,000 out17,657 toman

Price history

DateInput (toman/1M)Output (toman/1M)
14 Oct 202688,286353,144
28 Sep 2026103,866415,463
28 Sep 202688,286353,144
27 Sep 2026103,866415,463
27 Sep 202688,286353,144
24 Sep 2026103,866415,463
7 Sep 202688,286353,144

Every price change for this model is recorded. Toman figures use today's rate.

Frequently asked

How do I call MiniMax M2 from Iran?
Sign up with your mobile number, top the wallet up in toman, create an API key, then in the official OpenAI SDK point base_url at https://api.uttapen.ir/v1 and set model to "minimax/minimax-m2". Nothing else in your code changes.
What does MiniMax M2 cost in toman?
88,286 toman per 1M input tokens and 353,144 toman per 1M output tokens; a 1,000-word request is around 574 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does MiniMax M2 take?
Up to 204,800 tokens per request, roughly 154k words. A single answer can reach 131,072 tokens.
Does MiniMax M2 support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape.