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MiMo-V2.5 API: toman pricing and code

xiaomi/mimo-v2.5

Image inputTool callingReasoningJSON outputAudio input
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Input · per 1M tokens
138,488 toman
Output · per 1M tokens
692,439 toman
One 1,000-word request ≈
1,080 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 27,698 toman per 1M tokens.Pricing and top-ups

MiMo-V2.5 example: reading an image into JSON

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

# tip: a data URI works too — base64 the file and prefix it with data:image/jpeg;base64,
curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "xiaomi/mimo-v2.5",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Return only the invoice number and the total, as JSON."},
        {"type": "image_url", "image_url": {"url": "https://example.com/factor.jpg"}}
      ]
    }]
  }'
import base64, json
from openai import OpenAI

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

img = base64.b64encode(open("factor.jpg", "rb").read()).decode()

resp = client.chat.completions.create(
    model="xiaomi/mimo-v2.5",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Return the invoice number, the date and the total as JSON."},
            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}},
        ],
    }],
    response_format={"type": "json_object"},
)
print(json.loads(resp.choices[0].message.content))
import OpenAI from "openai";
import { readFileSync } from "node:fs";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const img = readFileSync("factor.jpg").toString("base64");

const resp = await client.chat.completions.create({
  model: "xiaomi/mimo-v2.5",
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "Return the invoice number, the date and the total as JSON." },
      { type: "image_url", image_url: { url: `data:image/jpeg;base64,${img}` } },
    ],
  }],
  response_format: { type: "json_object" },
});
console.log(JSON.parse(resp.choices[0].message.content));

What is MiMo-V2.5 good for?

Xiaomi publishes this model; we expose it under the id "xiaomi/mimo-v2.5". Its context window is 1,000,000 tokens, roughly 750k English words in one request. A single response can run to 65,536 tokens. On price it sits in the "mid-range" band — cheaper than 185 and dearer than 199 of the other paid models in the catalogue.

What you get on top of text in, text out: it reads images directly, which makes it a real option for invoices, forms and screenshots; it accepts audio input; 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.

Input runs at 138,488 toman per 1M tokens and output at 692,439 — output costs 5× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 27,698 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 Seed 1.6: MiMo-V2.5 works out about 10% more expensive, 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 93 thousand-word requests on MiMo-V2.5, and every 1,000 toman is about 926 words of round trip. A job with one million input tokens and one million output tokens comes to 830,926 toman in total. Filling this model's 1,000,000-token window costs 138,488 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "xiaomi/mimo-v2.6-pro", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 80% more expensive (1,080 against 587 toman). The context windows differ too: 1,000,000 against 1,050,000 tokens. Maximum answer length differs as well: 65,536 against 131,072 tokens. On parameters the other takes logit_bias, logprobs, min_p, repetition_penalty.

Xiaomi has 5 models in our catalogue; the cheapest is MiMo-V2.6-Flash at 189 toman per thousand words and the dearest MiMo-V2.6-Pro-UltraSpeed at 5,874. Among the less common parameters it accepts presence_penalty, top_k — all through the standard request body. It does not support seed, 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 Nova 2 Lite at 1,000,000 tokens.

Where it makes sense: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, pulling text and fields out of images, 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

MiMo-V2.5 is a native omnimodal model by Xiaomi. It delivers Pro-level agentic performance at roughly half the inference cost, while surpassing MiMo-V2-Omni in multimodal perception across image and video understanding...

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 out485 toman
Summarising a ten-page document4,000 in + 600 out969 toman
Classifying a thousand short rows120,000 in + 20,000 out30,467 toman

Price history

DateInput (toman/1M)Output (toman/1M)
27 Oct 2026138,488692,439
21 Oct 202641,20082,400
7 Sep 202648,47196,941

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

Frequently asked

How do I call MiMo-V2.5 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 "xiaomi/mimo-v2.5". Nothing else in your code changes.
What does MiMo-V2.5 cost in toman?
138,488 toman per 1M input tokens and 692,439 toman per 1M output tokens; a 1,000-word request is around 1,080 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does MiMo-V2.5 take?
Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 65,536 tokens.
Does MiMo-V2.5 support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.