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Ministral 3 3B 2512 API: toman pricing and code

mistralai/ministral-3b-2512

Image inputTool callingJSON output
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Input · per 1M tokens
34,622 toman
Output · per 1M tokens
34,622 toman
One 1,000-word request ≈
90 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 3,462 toman per 1M tokens.Pricing and top-ups

Ministral 3 3B 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-3b-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-3b-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-3b-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 3B 2512 good for?

The id for Ministral 3 3B 2512 in our API is "mistralai/ministral-3b-2512", served from Mistral. Context is 131,072 tokens; past that you have to summarise the history yourself. A single response can run to 104,857 tokens. On price it sits in the "very cheap" band — cheaper than 383 and dearer than 12 of the other paid models in the catalogue.

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. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.

Pricing is 34,622 toman per 1M input tokens and 34,622 per 1M output tokens. A 1,000-word round trip on Ministral 3 3B 2512 lands near 90 toman. It supports cached input: repeated context is billed at 3,462 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 Reka Edge, and Ministral 3 3B 2512 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 1,111 thousand-word requests on Ministral 3 3B 2512, and every 1,000 toman is about 11,111 words of round trip. A job with one million input tokens and one million output tokens comes to 69,244 toman in total. Filling this model's 131,072-token window costs 4,538 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "mistralai/mistral-small-3.2-24b-instruct", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 29% cheaper (90 against 124 toman). The context windows differ too: 131,072 against 256,000 tokens. Maximum answer length differs as well: 104,857 against 16,384 tokens. On parameters the other takes logit_bias, logprobs, min_p, repetition_penalty.

Mistral has 21 models in our catalogue; the cheapest is Mistral Nemo at 27 toman per thousand words and the dearest Mistral Medium 3.5 at 4,051. Among the less common parameters it accepts presence_penalty — all through the standard request body. 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 Aion-2.0 at 131,072 tokens.

Good fits: 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.

Provider's own description

The smallest model in the Ministral 3 family, Ministral 3 3B 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.

JobTokensCost
One chat turn with a medium history1,500 in + 400 out66 toman
Summarising a ten-page document4,000 in + 600 out159 toman
Classifying a thousand short rows120,000 in + 20,000 out4,847 toman

Frequently asked

How do I call Ministral 3 3B 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-3b-2512". Nothing else in your code changes.
What does Ministral 3 3B 2512 cost in toman?
34,622 toman per 1M input tokens and 34,622 toman per 1M output tokens; a 1,000-word request is around 90 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Ministral 3 3B 2512 take?
Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 104,857 tokens.
Does Ministral 3 3B 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.