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MiniMax M3.1 Flash API: toman pricing and code

minimax/minimax-m3.1-flash

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
51,933 toman
Output · per 1M tokens
207,732 toman
One 1,000-word request ≈
338 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 5,193 toman per 1M tokens.Pricing and top-ups

MiniMax M3.1 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": "minimax/minimax-m3.1-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="minimax/minimax-m3.1-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: "minimax/minimax-m3.1-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 MiniMax M3.1 Flash good for?

MiniMax M3.1 Flash is one of MiniMax's models. Put "minimax/minimax-m3.1-flash" in the model field and the rest of your code stays as it is. MiniMax M3.1 Flash keeps 1,048,576 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 524,288 tokens. On price it sits in the "cheap" band — cheaper than 282 and dearer than 103 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 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 51,933 toman per 1M tokens and output at 207,732 — output costs 4× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 5,193 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 Mistral Small 4, and MiniMax M3.1 Flash costs about the same, 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 296 thousand-word requests on MiniMax M3.1 Flash, and every 1,000 toman is about 2,959 words of round trip. A job with one million input tokens and one million output tokens comes to 259,665 toman in total. Filling this model's 1,048,576-token window costs 54,456 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-m3", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 2× cheaper (338 against 675 toman). Maximum answer length differs as well: 524,288 against 512,000 tokens. On parameters, this one takes reasoning_effort.

MiniMax has 9 models in our catalogue; the cheapest is MiniMax M3.1 Flash at 338 toman per thousand words and the dearest MiniMax M1 at 1,238. Among the less common parameters it accepts logit_bias, logprobs, min_p, presence_penalty, reasoning_effort, repetition_penalty — all through the standard request body. By context size the nearest option from another provider is DeepSeek V4 Flash 0423 at 1,048,576 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.

Provider's own description

MiniMax-M3.1-Flash is a multimodal model from MiniMax. Compared with M3, it delivers significant improvements across multiple benchmarks, particularly in complex agentic tasks, coding, and cybersecurity. It is built for complex,...

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 out161 toman
Summarising a ten-page document4,000 in + 600 out332 toman
Classifying a thousand short rows120,000 in + 20,000 out10,387 toman

Price history

DateInput (toman/1M)Output (toman/1M)
5 Nov 202651,933207,732
4 Nov 202625,966103,866
31 Oct 202651,933207,732
31 Oct 202625,966103,866
22 Oct 202651,933207,732

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

Frequently asked

How do I call MiniMax M3.1 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 "minimax/minimax-m3.1-flash". Nothing else in your code changes.
What does MiniMax M3.1 Flash cost in toman?
51,933 toman per 1M input tokens and 207,732 toman per 1M output tokens; a 1,000-word request is around 338 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does MiniMax M3.1 Flash take?
Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 524,288 tokens.
Does MiniMax M3.1 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.