MiniMax M3 API: toman pricing and code
minimax/minimax-m3
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 20,773 toman per 1M tokens.Pricing and top-ups
MiniMax M3 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",
"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",
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",
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 good for?
MiniMax publishes this model; we expose it under the id "minimax/
What it can do beyond plain text: 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.
Pricing is 103,866 toman per 1M input tokens and 415,463 per 1M output tokens. A 1,000-word round trip on MiniMax M3 lands near 675 toman. It supports cached input: repeated context is billed at 20,773 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 Muse Glimmer 30B, and MiniMax M3 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 148 thousand-word requests on MiniMax M3, and every 1,000 toman is about 1,481 words of round trip. A job with one million input tokens and one million output tokens comes to 519,329 toman in total. Filling this model's 1,048,576-token window costs 108,911 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 has 9 models in our catalogue; the cheapest is MiniMax M3.1 Flash at 169 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, repetition_penalty, top_k — 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.
Good fits: 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.
MiniMax-M3 is a multimodal foundation model from MiniMax. It supports text, image, and video inputs with text output, a 1M-token context window, and is suited for long-horizon agentic work, coding,...
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 | 322 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 665 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 20,773 toman |
Frequently asked
- How do I call MiniMax M3 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". Nothing else in your code changes. - What does MiniMax M3 cost in toman?
- 103,866 toman per 1M input tokens and 415,463 toman per 1M output tokens; a 1,000-word request is around 675 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does MiniMax M3 take?
- Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 512,000 tokens.
- Does MiniMax M3 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.