MiniMax M3 (batch) API: toman pricing and code
minimax/minimax-m3:batch
visiontoolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 17,408 toman / 1M.Pricing and top-ups
MiniMax M3 (batch) 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:batch",
"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:batch",
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:batch",
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 (batch) good for?
MiniMax M3 (batch) is one of MiniMax's models. Put "minimax/minimax-m3:batch" in the model field and the rest of your code stays as it is. MiniMax M3 (batch) keeps 524,288 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 471,859 tokens. On price it sits in the "cheap" band — cheaper than 151 and dearer than 255 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 87,038 toman per 1M tokens and output at 348,150 — output costs 4× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 17,408 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 GPT-5.6 Luna: MiniMax M3 (batch) 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 177 thousand-word requests on MiniMax M3 (batch), and every 1,000 toman is about 1,767 words of round trip. A job with one million input tokens and one million output tokens comes to 435,188 toman in total. Filling this model's 524,288-token window costs 45,633 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "minimax/minimax-m3", because the underlying model is the same. The difference is the suffix: "batch" against the standard variant. Both land at nearly the same price on a thousand-word request (566 and 566 toman). The context windows differ too: 524,288 against 1,048,576 tokens. Maximum answer length differs as well: 471,859 against 512,000 tokens. On parameters the other takes logprobs, seed, top_logprobs.
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, min_p, repetition_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 Inkling Small (batch) at 524,288 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.
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 | 270 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 557 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 17,408 toman |
Other variants of this model
Same core model, different execution terms and different price. This page is the batch (cheaper, slower) variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| standard | minimax/minimax-m3 | 348,150 | 1,048,576 |
| free | minimax/minimax-m3:free | 0 | 1,048,576 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Frequently asked
- How do I call MiniMax M3 (batch) 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:batch". Nothing else in your code changes.
- What does MiniMax M3 (batch) cost in toman?
- 87,038 toman per 1M input tokens and 348,150 toman per 1M output tokens; a 1,000-word request is around 566 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does MiniMax M3 (batch) take?
- Up to 524,288 tokens per request, roughly 393k words. A single answer can reach 471,859 tokens.
- Does MiniMax M3 (batch) 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.