Ministral 3 8B 2512 API: toman pricing and code
mistralai/ministral-8b-2512
visiontoolsjsonYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 4,352 toman / 1M.Pricing and top-ups
Ministral 3 8B 2512 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": "mistralai/ministral-8b-2512",
"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="mistralai/ministral-8b-2512",
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: "mistralai/ministral-8b-2512",
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 Ministral 3 8B 2512 good for?
Mistral publishes this model; we expose it under the id "mistralai/ministral-8b-2512". Its context window is 262,144 tokens, roughly 197k English words in one request. A single response can run to 209,715 tokens. On price it sits in the "very cheap" band — cheaper than 19 and dearer than 387 of the other paid models in the catalogue.
Capabilities available on this id: 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. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.
For a back-of-envelope figure: about 113 toman per 1,000-word exchange (43,519 in, 43,519 out, per 1M tokens). It supports cached input: repeated context is billed at 4,352 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 Gemma 3 12B: Ministral 3 8B 2512 works out roughly 1.5× 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 885 thousand-word requests on Ministral 3 8B 2512, and every 1,000 toman is about 8,850 words of round trip. A job with one million input tokens and one million output tokens comes to 87,038 toman in total. Filling this model's 262,144-token window costs 11,408 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "mistralai/ministral-14b-2512", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 1.3× cheaper (113 against 151 toman).
Mistral has 20 models in our catalogue; the cheapest is Mistral Nemo at 18 toman per thousand words and the dearest Mistral Medium 3.5 at 3,394. It does not support include_reasoning, reasoning, 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 Trinity Large Thinking at 262,144 tokens.
What to use it for: high-volume work such as classification, tagging and bulk summarising, 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.
A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.
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 | 83 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 200 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 6,093 toman |
Frequently asked
- How do I call Ministral 3 8B 2512 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 "mistralai/ministral-8b-2512". Nothing else in your code changes.
- What does Ministral 3 8B 2512 cost in toman?
- 43,519 toman per 1M input tokens and 43,519 toman per 1M output tokens; a 1,000-word request is around 113 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Ministral 3 8B 2512 take?
- Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 209,715 tokens.
- Does Ministral 3 8B 2512 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.