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MiniMax M2.7 API: toman pricing and code

minimax/minimax-m2.7

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: 17,408 toman / 1M.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))

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 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 M2.7 lands near 566 toman. It supports cached input: repeated context is billed at 17,408 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 M2.7 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 M2.7, 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.

This id gets confused with "minimax/minimax-m2.7:free", because the underlying model is the same. The difference is the suffix: no suffix (the standard variant) against "free". The context windows differ too: 204,800 against 196,608 tokens. Maximum answer length differs as well: 131,072 against 176,947 tokens. On parameters, this one takes frequency_penalty, logit_bias, logprobs, min_p.

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 logit_bias, logprobs, min_p, repetition_penalty, top_k, top_logprobs — all through the standard request body. 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 out270 toman
Summarising a ten-page document4,000 in + 600 out557 toman
Classifying a thousand short rows120,000 in + 20,000 out17,408 toman

Other variants of this model

Same core model, different execution terms and different price. This page is the standard variant.

VariantModel idOutput / 1MContext
freeminimax/minimax-m2.7:free0196,608

Put the variant's id verbatim in the model field; nothing else in your code changes.

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?
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 M2.7 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.