uttapen
Create an API key

Mixtral 8x22B Instruct API: toman pricing and code

mistralai/mixtral-8x22b-instruct

toolsjsonfiles
Create a keyTry it
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

Mixtral 8x22B Instruct example: JSON output against a schema

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-…")

schema = {
    "name": "ticket",
    "schema": {
        "type": "object",
        "properties": {
            "category": {"type": "string", "enum": ["fani", "mali", "forush"]},
            "priority": {"type": "integer", "minimum": 1, "maximum": 5},
            "summary": {"type": "string"},
        },
        "required": ["category", "priority", "summary"],
        "additionalProperties": False,
    },
    "strict": True,
}

resp = client.chat.completions.create(
    model="mistralai/mixtral-8x22b-instruct",
    messages=[{"role": "user", "content": "Ticket: "For two days I cannot download my invoice and I was charged twice.""}],
    response_format={"type": "json_schema", "json_schema": schema},
)
print(json.loads(resp.choices[0].message.content))

What is Mixtral 8x22B Instruct good for?

Mixtral 8x22B Instruct is one of Mistral's models. Put "mistralai/mixtral-8x22b-instruct" in the model field and the rest of your code stays as it is. Mixtral 8x22B Instruct keeps 65,536 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 52,428 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.

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 580,250 toman per 1M input tokens and 1,740,750 per 1M output tokens. A 1,000-word round trip on Mixtral 8x22B Instruct lands near 3,017 toman. 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: Mixtral 8x22B Instruct 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 Mixtral 8x22B Instruct, 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 65,536-token window costs 38,027 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 (3,017 and 3,017 toman). The context windows differ too: 65,536 against 128,000 tokens. Maximum answer length differs as well: 52,428 against 102,400 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 Nano Banana Pro (Gemini 3 Pro Image Preview) at 65,536 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

Mistral's official instruct fine-tuned version of [Mixtral 8x22B](/models/mistralai/mixtral-8x22b). It uses 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. Its strengths include: - strong math, coding,...

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 Mixtral 8x22B Instruct 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/mixtral-8x22b-instruct". Nothing else in your code changes.
What does Mixtral 8x22B Instruct 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 Mixtral 8x22B Instruct take?
Up to 65,536 tokens per request, roughly 49k words. A single answer can reach 52,428 tokens.
Does Mixtral 8x22B Instruct 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.