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MiniMax M2.7 API: toman pricing and code

minimax/minimax-m2.7

Tool callingReasoningJSON output
Create a keyTry it
Input · per 1M tokens
103,866 toman
Output · per 1M tokens
415,463 toman
One 1,000-word request ≈
675 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 20,773 toman per 1M tokens.Pricing and top-ups

MiniMax M2.7 example: JSON output against a schema

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-…")

schema = {
    "name": "ticket",
    "schema": {
        "type": "object",
        "properties": {
            "category": {"type": "string", "enum": ["fani", "mali", "forush"]},
            "priority": {"type": "integer", "minimum": 1, "maximum": 5},
            "summary": {"type": "string"},
        },
        "required": ["category", "priority", "summary"],
        "additionalProperties": False,
    },
    "strict": True,
}

resp = client.chat.completions.create(
    model="minimax/minimax-m2.7",
    messages=[{"role": "user", "content": "Ticket: "For two days I cannot download my invoice and I was charged twice.""}],
    response_format={"type": "json_schema", "json_schema": schema},
)
print(json.loads(resp.choices[0].message.content))
import OpenAI from "openai";

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

const resp = await client.chat.completions.create({
  model: "minimax/minimax-m2.7",
  messages: [{ role: "user", content: "Ticket: "For two days I cannot download my invoice and I was charged twice."" }],
  response_format: {
    type: "json_schema",
    json_schema: {
      name: "ticket",
      strict: true,
      schema: {
        type: "object",
        properties: {
          category: { type: "string", enum: ["fani", "mali", "forush"] },
          priority: { type: "integer", minimum: 1, maximum: 5 },
          summary: { type: "string" },
        },
        required: ["category", "priority", "summary"],
        additionalProperties: false,
      },
    },
  },
});
console.log(JSON.parse(resp.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-m2.7",
    "messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
    "response_format": {"type": "json_object"}
  }'

What is MiniMax M2.7 good for?

MiniMax publishes this model; we expose it under the id "minimax/minimax-m2.7". Its context window is 204,800 tokens, roughly 154k English words in one request. A single response can run to 131,072 tokens. On price it sits in the "cheap" band — cheaper than 232 and dearer than 157 of the other paid models in the catalogue.

What it can do beyond plain text: it supports tool calling, so it can invoke your own functions with valid arguments; it returns schema-valid JSON through response_format, ready to hand to your code; 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.

Pricing is 103,866 toman per 1M input tokens and 415,463 per 1M output tokens. A 1,000-word round trip on MiniMax M2.7 lands near 675 toman. It supports cached input: repeated context is billed at 20,773 toman per 1M, which matters a lot if your system prompt is long. 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 LongCat 2.0, and MiniMax M2.7 costs about the same, and its context window is smaller. 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 148 thousand-word requests on MiniMax M2.7, and every 1,000 toman is about 1,481 words of round trip. A job with one million input tokens and one million output tokens comes to 519,329 toman in total. Filling this model's 204,800-token window costs 21,272 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-m2.1", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (675 and 675 toman). On parameters, this one takes frequency_penalty, logit_bias, min_p, presence_penalty.

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. Among the less common parameters it accepts logit_bias, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. It does not support 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.

Good fits: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, agents that reach out to APIs and databases, extracting data against a fixed schema, analysing a long document or codebase in one request.

Provider's own description

MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity and continuous improvement. Built to actively participate in its own evolution, M2.7 integrates advanced agentic capabilities through multi-agent...

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 out322 toman
Summarising a ten-page document4,000 in + 600 out665 toman
Classifying a thousand short rows120,000 in + 20,000 out20,773 toman

Price history

DateInput (toman/1M)Output (toman/1M)
13 Oct 2026103,866415,463
29 Sep 202672,706290,824
28 Sep 2026103,866415,463
27 Sep 202672,706290,824
7 Sep 2026103,866415,463

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

Frequently asked

How do I call MiniMax M2.7 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-m2.7". Nothing else in your code changes.
What does MiniMax M2.7 cost in toman?
103,866 toman per 1M input tokens and 415,463 toman per 1M output tokens; a 1,000-word request is around 675 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does MiniMax M2.7 take?
Up to 204,800 tokens per request, roughly 154k words. A single answer can reach 131,072 tokens.
Does MiniMax M2.7 support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Structured output through response_format works too.