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Mistral Large API: toman pricing and code

mistralai/mistral-large

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Input · per 1M tokens
580,250 toman
Output · per 1M tokens
1,740,750 toman
One 1,000-word request ≈
3,017 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 58,025 toman / 1M.Pricing and top-ups

Mistral Large example: tool calling

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

from openai import OpenAI
import json

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

tools = [{
    "type": "function",
    "function": {
        "name": "check_stock",
        "description": "Returns the stock level of a product",
        "parameters": {
            "type": "object",
            "properties": {"sku": {"type": "string"}},
            "required": ["sku"],
        },
    },
}]

messages = [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}]
first = client.chat.completions.create(model="mistralai/mistral-large", messages=messages, tools=tools)
call = first.choices[0].message.tool_calls[0]

# you run the function yourself — the model never touches the database
args = json.loads(call.function.arguments)
result = {"sku": args["sku"], "qty": 7}

messages += [first.choices[0].message, {"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)}]
final = client.chat.completions.create(model="mistralai/mistral-large", messages=messages, tools=tools)
print(final.choices[0].message.content)

What is Mistral Large good for?

Mistral Large comes from Mistral; in uttapen you reach it with the model id "mistralai/mistral-large". It accepts up to 128,000 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 102,400 tokens. On price it sits in the "mid-range" band — cheaper than 297 and dearer than 109 of the other paid models in the catalogue.

Capabilities available on this id: 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.

For a back-of-envelope figure: about 3,017 toman per 1,000-word exchange (580,250 in, 1,740,750 out, per 1M tokens). It supports cached input: repeated context is billed at 58,025 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 Aion-3.0: Mistral Large works out roughly 1.1× cheaper, 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 33 thousand-word requests on Mistral Large, and every 1,000 toman is about 331 words of round trip. A job with one million input tokens and one million output tokens comes to 2,321,000 toman in total. Filling this model's 128,000-token window costs 74,272 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-large-2407", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (3,017 and 3,017 toman). The context windows differ too: 128,000 against 131,072 tokens. Maximum answer length differs as well: 102,400 against 104,857 tokens.

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 Nova Micro 1.0 at 128,000 tokens.

What to use it for: product chatbots and internal assistants, where cost and quality have to balance, agents that reach out to APIs and databases, extracting data against a fixed schema.

Provider's own description

This is Mistral AI's flagship model, Mistral Large 2 (version `mistral-large-2407`). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement [here](https://mistral.ai/news/mistral-large-2407/)....

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 out1,567 toman
Summarising a ten-page document4,000 in + 600 out3,365 toman
Classifying a thousand short rows120,000 in + 20,000 out104,445 toman

Frequently asked

How do I call Mistral Large 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". Nothing else in your code changes.
What does Mistral Large cost in toman?
580,250 toman per 1M input tokens and 1,740,750 toman per 1M output tokens; a 1,000-word request is around 3,017 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Mistral Large take?
Up to 128,000 tokens per request, roughly 96k words. A single answer can reach 102,400 tokens.
Does Mistral Large support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Structured output through response_format works too.