MiMo-V2.6-Flash API: toman pricing and code
xiaomi/mimo-v2.6-flash
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 969 toman per 1M tokens.Pricing and top-ups
MiMo-V2.6-Flash 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-v2.6-flash",
"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-v2.6-flash",
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-v2.6-flash",
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-V2.6-Flash good for?
MiMo-V2.6-Flash is one of Xiaomi's models. Put "xiaomi/
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 48,471 toman per 1M tokens and output at 96,941 — output costs 2× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 969 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 Flash: MiMo-V2.6-Flash 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 529 thousand-word requests on MiMo-V2.6-Flash, and every 1,000 toman is about 5,291 words of round trip. A job with one million input tokens and one million output tokens comes to 145,412 toman in total. Filling this model's 1,050,000-token window costs 50,894 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "xiaomi/
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 logit_bias, logprobs, min_p, parallel_tool_calls, presence_penalty, repetition_penalty — all through the standard request body. By context size the nearest option from another provider is GPT-5.4 at 1,050,000 tokens.
Where it makes sense: high-volume work such as classification, tagging and bulk summarising, 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.
MiMo-V2.6-Flash is an open-source foundation model developed by Xiaomi. Built on a Mixture-of-Experts architecture with 309B total parameters and 15B activated per token, it employs a hybrid attention mechanism for...
Three real jobs, priced on this model
Each figure is derived from the prices above and moves when they do.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 111 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 252 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 7,755 toman |
Frequently asked
- How do I call MiMo-V2.6-Flash 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-v2.6-flash". Nothing else in your code changes. - What does MiMo-V2.6-Flash cost in toman?
- 48,471 toman per 1M input tokens and 96,941 toman per 1M output tokens; a 1,000-word request is around 189 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does MiMo-V2.6-Flash take?
- Up to 1,050,000 tokens per request, roughly 788k words. A single answer can reach 131,072 tokens.
- Does MiMo-V2.6-Flash 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.