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Mistral Small 3.2 24B API: toman pricing and code

mistralai/mistral-small-3.2-24b-instruct

Image inputTool callingJSON output
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Input · per 1M tokens
25,966 toman
Output · per 1M tokens
69,244 toman
One 1,000-word request ≈
124 toman

You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups

Mistral Small 3.2 24B 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/mistral-small-3.2-24b-instruct",
    "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/mistral-small-3.2-24b-instruct",
    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/mistral-small-3.2-24b-instruct",
  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 Mistral Small 3.2 24B good for?

The id for Mistral Small 3.2 24B in our API is "mistralai/mistral-small-3.2-24b-instruct", served from Mistral. Context is 256,000 tokens; past that you have to summarise the history yourself. A single response can run to 16,384 tokens. On price it sits in the "very cheap" band — cheaper than 353 and dearer than 36 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 124 toman per 1,000-word exchange (25,966 in, 69,244 out, per 1M tokens). 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 Nano (batch): Mistral Small 3.2 24B works out about 20% 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 806 thousand-word requests on Mistral Small 3.2 24B, and every 1,000 toman is about 8,065 words of round trip. A job with one million input tokens and one million output tokens comes to 95,210 toman in total. Filling this model's 256,000-token window costs 6,647 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-8b-2512", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (124 and 135 toman). The context windows differ too: 256,000 against 262,144 tokens. Maximum answer length differs as well: 16,384 against 209,715 tokens. On parameters, this one 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 logit_bias, logprobs, min_p, presence_penalty, repetition_penalty, top_k — 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 Command A at 256,000 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.

Provider's own description

Mistral-Small-3.2-24B-Instruct-2506 is an updated 24B parameter model from Mistral optimized for instruction following, repetition reduction, and improved function calling. Compared to the 3.1 release, version 3.2 significantly improves accuracy on...

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 out67 toman
Summarising a ten-page document4,000 in + 600 out145 toman
Classifying a thousand short rows120,000 in + 20,000 out4,501 toman

Price history

DateInput (toman/1M)Output (toman/1M)
23 Oct 202625,96669,244
15 Oct 202632,45886,555
15 Oct 202625,96669,244
16 Sep 202632,45886,555
7 Sep 202625,96669,244

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

Frequently asked

How do I call Mistral Small 3.2 24B 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/mistral-small-3.2-24b-instruct". Nothing else in your code changes.
What does Mistral Small 3.2 24B cost in toman?
25,966 toman per 1M input tokens and 69,244 toman per 1M output tokens; a 1,000-word request is around 124 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Mistral Small 3.2 24B take?
Up to 256,000 tokens per request, roughly 192k words. A single answer can reach 16,384 tokens.
Does Mistral Small 3.2 24B 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.