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Mistral Large 2407 API: toman pricing and code

mistralai/mistral-large-2407

Tool callingJSON outputFile input
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Input · per 1M tokens
692,439 toman
Output · per 1M tokens
2,077,316 toman
One 1,000-word request ≈
3,601 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 69,244 toman per 1M tokens.Pricing and top-ups

Mistral Large 2407 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-2407", 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-2407", messages=messages, tools=tools)
print(final.choices[0].message.content)
import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });

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

const messages = [{ role: "user", content: "How many of the Nike NK-42 shoe are in stock?" }];
const first = await client.chat.completions.create({ model: "mistralai/mistral-large-2407", messages, tools });
const call = first.choices[0].message.tool_calls[0];

const args = JSON.parse(call.function.arguments);
const result = { sku: args.sku, qty: 7 };

messages.push(first.choices[0].message, { role: "tool", tool_call_id: call.id, content: JSON.stringify(result) });
const final = await client.chat.completions.create({ model: "mistralai/mistral-large-2407", messages, tools });
console.log(final.choices[0].message.content);
curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistralai/mistral-large-2407",
    "messages": [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "check_stock",
        "parameters": {"type": "object", "properties": {"sku": {"type": "string"}}, "required": ["sku"]}
      }
    }]
  }'

What is Mistral Large 2407 good for?

Mistral Large 2407 comes from Mistral; in uttapen you reach it with the model id "mistralai/mistral-large-2407". It accepts up to 131,072 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 104,857 tokens. On price it sits in the "mid-range" band — cheaper than 92 and dearer than 291 of the other paid models in the catalogue.

What it can do beyond plain text: 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 692,439 toman per 1M input tokens and 2,077,316 per 1M output tokens. A 1,000-word round trip on Mistral Large 2407 lands near 3,601 toman. It supports cached input: repeated context is billed at 69,244 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 2407 works out about 9% cheaper, and its context window is the same size. 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 28 thousand-word requests on Mistral Large 2407, and every 1,000 toman is about 278 words of round trip. A job with one million input tokens and one million output tokens comes to 2,769,755 toman in total. Filling this model's 131,072-token window costs 90,759 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", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (3,601 and 3,601 toman). The context windows differ too: 131,072 against 128,000 tokens. Maximum answer length differs as well: 104,857 against 102,400 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-2.0 at 131,072 tokens.

Good fits: 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 out1,870 toman
Summarising a ten-page document4,000 in + 600 out4,016 toman
Classifying a thousand short rows120,000 in + 20,000 out124,639 toman

Frequently asked

How do I call Mistral Large 2407 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-2407". Nothing else in your code changes.
What does Mistral Large 2407 cost in toman?
692,439 toman per 1M input tokens and 2,077,316 toman per 1M output tokens; a 1,000-word request is around 3,601 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Mistral Large 2407 take?
Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 104,857 tokens.
Does Mistral Large 2407 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.