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MiniMax M2.1 API: toman pricing and code

minimax/minimax-m2.1

toolsreasoningjson
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Input · per 1M tokens
87,038 toman
Output · per 1M tokens
348,150 toman
One 1,000-word request ≈
566 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 8,704 toman / 1M.Pricing and top-ups

MiniMax M2.1 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-m2.1",
    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))

What is MiniMax M2.1 good for?

MiniMax M2.1 is one of MiniMax's models. Put "minimax/minimax-m2.1" in the model field and the rest of your code stays as it is. MiniMax M2.1 keeps 204,800 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 131,072 tokens. On price it sits in the "cheap" band — cheaper than 151 and dearer than 255 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 87,038 toman per 1M input tokens and 348,150 per 1M output tokens. A 1,000-word round trip on MiniMax M2.1 lands near 566 toman. It supports cached input: repeated context is billed at 8,704 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 KAT-Coder-Pro V2: MiniMax M2.1 works out roughly 1× more expensive, 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 177 thousand-word requests on MiniMax M2.1, and every 1,000 toman is about 1,767 words of round trip. A job with one million input tokens and one million output tokens comes to 435,188 toman in total. Filling this model's 204,800-token window costs 17,825 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-m2.7", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (566 and 566 toman). On parameters the other takes logit_bias, logprobs, min_p, structured_outputs.

MiniMax has 11 models in our catalogue; the cheapest is MiniMax M2 at 481 toman per thousand words and the dearest MiniMax M1 at 1,037. Among the less common parameters it accepts 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-M2.1 is a lightweight, state-of-the-art large language model optimized for coding, agentic workflows, and modern application development. With only 10 billion activated parameters, it delivers a major jump in real-world...

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 out270 toman
Summarising a ten-page document4,000 in + 600 out557 toman
Classifying a thousand short rows120,000 in + 20,000 out17,408 toman

Frequently asked

How do I call MiniMax M2.1 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-m2.1". Nothing else in your code changes.
What does MiniMax M2.1 cost in toman?
87,038 toman per 1M input tokens and 348,150 toman per 1M output tokens; a 1,000-word request is around 566 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does MiniMax M2.1 take?
Up to 204,800 tokens per request, roughly 154k words. A single answer can reach 131,072 tokens.
Does MiniMax M2.1 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.