How do I call Mistral Large 4 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-4-0". Nothing else in your code changes.

What does Mistral Large 4 cost in toman?

242,354 toman per 1M input tokens and 727,061 toman per 1M output tokens; a 1,000-word request is around 1,260 toman. You pay for the usage that request actually reported, and a failed request costs nothing.

How much input does Mistral Large 4 take?

Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 262,144 tokens.

Does Mistral Large 4 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.

Mistral Large 4 API: toman pricing and code

mistralai/mistral-large-4-0

Image inputTool callingReasoningJSON output

Create a key Try it

Input · per 1M tokens

242,354 toman

Output · per 1M tokens

727,061 toman

One 1,000-word request ≈

1,260 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 24,235 toman per 1M tokens.Pricing and top-ups

Mistral Large 4 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.

curlPythonNode.js

# 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-4-0",
    "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-4-0",
    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-4-0",
  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 4 good for?

Mistral Large 4 comes from Mistral; in uttapen you reach it with the model id "mistralai/mistral-large-4-0". It accepts up to 1,048,576 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 262,144 tokens. On price it sits in the "mid-range" band — cheaper than 183 and dearer than 215 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 242,354 toman per 1M tokens and output at 727,061 — output costs 3× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 24,235 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 Qwen3.5-122B-A10B: Mistral Large 4 works out about 20% 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 79 thousand-word requests on Mistral Large 4, and every 1,000 toman is about 794 words of round trip. A job with one million input tokens and one million output tokens comes to 969,414 toman in total. Filling this model's 1,048,576-token window costs 254,126 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-3-5:batch", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 38% cheaper (1,260 against 2,025 toman). The context windows differ too: 1,048,576 against 262,144 tokens. Maximum answer length differs as well: 262,144 against 209,715 tokens. On parameters, this one takes logprobs, top_logprobs.

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 logprobs, presence_penalty, reasoning_effort, top_logprobs — all through the standard request body. By context size the nearest option from another provider is DeepSeek V4 Flash 0423 at 1,048,576 tokens.

Where it makes sense: product chatbots and internal assistants, where cost and quality have to balance, 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.

Provider's own description

Mistral Large 4 is a frontier multimodal (text and image input) model from Mistral AI built for reasoning, coding, and agentic workloads. It offers a 1M-token context window with up...

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	654 toman
Summarising a ten-page document	4,000 in + 600 out	1,406 toman
Classifying a thousand short rows	120,000 in + 20,000 out	43,624 toman

Job

Tokens

Cost

One chat turn with a medium history

1,500 in + 400 out

654 toman

Summarising a ten-page document

4,000 in + 600 out

1,406 toman

Classifying a thousand short rows

120,000 in + 20,000 out

43,624 toman

Price history

Date	Input (toman/1M)	Output (toman/1M)
29 Oct 2026	242,354	727,061
20 Oct 2026	470,858	1,447,197
6 Oct 2026	235,429	723,598

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

Frequently asked

How do I call Mistral Large 4 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-4-0". Nothing else in your code changes.
What does Mistral Large 4 cost in toman?: 242,354 toman per 1M input tokens and 727,061 toman per 1M output tokens; a 1,000-word request is around 1,260 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Mistral Large 4 take?: Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 262,144 tokens.
Does Mistral Large 4 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.