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MiniMax M2.5 API: toman pricing and code

minimax/minimax-m2.5

toolsreasoningjson
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Input · per 1M tokens
78,334 toman
Output · per 1M tokens
313,335 toman
One 1,000-word request ≈
509 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 7,833 toman / 1M.Pricing and top-ups

MiniMax M2.5 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-m2.5", 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-m2.5", messages=messages, tools=tools)
print(final.choices[0].message.content)

What is MiniMax M2.5 good for?

MiniMax M2.5 is one of MiniMax's models. Put "minimax/minimax-m2.5" in the model field and the rest of your code stays as it is. MiniMax M2.5 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 128,000 tokens. On price it sits in the "cheap" band — cheaper than 145 and dearer than 261 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 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.

For a back-of-envelope figure: about 509 toman per 1,000-word exchange (78,334 in, 313,335 out, per 1M tokens). It supports cached input: repeated context is billed at 7,833 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 Qwen3 Next 80B A3B Instruct: MiniMax M2.5 works out roughly 1.1× 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 196 thousand-word requests on MiniMax M2.5, and every 1,000 toman is about 1,965 words of round trip. A job with one million input tokens and one million output tokens comes to 391,669 toman in total. Filling this model's 204,800-token window costs 16,043 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", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (509 and 481 toman). Maximum answer length differs as well: 128,000 against 131,072 tokens. On parameters, this one takes logit_bias, 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.

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, extracting data against a fixed schema, analysing a long document or codebase in one request.

Provider's own description

MiniMax-M2.5 is a SOTA large language model designed for real-world productivity. Trained in a diverse range of complex real-world digital working environments, M2.5 builds upon the coding expertise of M2.1...

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 out243 toman
Summarising a ten-page document4,000 in + 600 out501 toman
Classifying a thousand short rows120,000 in + 20,000 out15,667 toman

Frequently asked

How do I call MiniMax M2.5 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-m2.5". Nothing else in your code changes.
What does MiniMax M2.5 cost in toman?
78,334 toman per 1M input tokens and 313,335 toman per 1M output tokens; a 1,000-word request is around 509 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does MiniMax M2.5 take?
Up to 204,800 tokens per request, roughly 154k words. A single answer can reach 128,000 tokens.
Does MiniMax M2.5 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.