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Mistral Large 3 2512 API: toman pricing and code

mistralai/mistral-large-2512

Image inputTool callingJSON outputFile input
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Input · per 1M tokens
173,110 toman
Output · per 1M tokens
519,329 toman
One 1,000-word request ≈
900 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 17,311 toman per 1M tokens.Pricing and top-ups

Mistral Large 3 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/mistral-large-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/mistral-large-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/mistral-large-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 Mistral Large 3 2512 good for?

Mistral Large 3 2512 is one of Mistral's models. Put "mistralai/mistral-large-2512" in the model field and the rest of your code stays as it is. Mistral Large 3 2512 keeps 262,144 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 209,715 tokens. On price it sits in the "cheap" band — cheaper than 213 and dearer than 178 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 takes files such as PDFs as input; 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 173,110 toman per 1M input tokens and 519,329 per 1M output tokens. A 1,000-word round trip on Mistral Large 3 2512 lands near 900 toman. It supports cached input: repeated context is billed at 17,311 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 Command A+: Mistral Large 3 2512 works out about 10% 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 111 thousand-word requests on Mistral Large 3 2512, and every 1,000 toman is about 1,111 words of round trip. A job with one million input tokens and one million output tokens comes to 692,439 toman in total. Filling this model's 262,144-token window costs 45,380 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-medium-3", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 17% cheaper (900 against 1,080 toman). The context windows differ too: 262,144 against 131,072 tokens. Maximum answer length differs as well: 209,715 against 104,857 tokens.

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 3.5 at 262,144 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, analysing a long document or codebase in one request.

Provider's own description

Mistral Large 3 2512 is Mistral’s most capable model to date, featuring a sparse mixture-of-experts architecture with 41B active parameters (675B total), and released under the Apache 2.0 license.

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 out467 toman
Summarising a ten-page document4,000 in + 600 out1,004 toman
Classifying a thousand short rows120,000 in + 20,000 out31,160 toman

Price history

DateInput (toman/1M)Output (toman/1M)
25 Sep 2026173,110519,329
18 Sep 2026190,421571,262
7 Sep 2026173,110519,329

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

Frequently asked

How do I call Mistral Large 3 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/mistral-large-2512". Nothing else in your code changes.
What does Mistral Large 3 2512 cost in toman?
173,110 toman per 1M input tokens and 519,329 toman per 1M output tokens; a 1,000-word request is around 900 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Mistral Large 3 2512 take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 209,715 tokens.
Does Mistral Large 3 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.