Mistral Medium 3.1 API: toman pricing and code
mistralai/mistral-medium-3.1
visiontoolsjsonfilesYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 11,605 toman / 1M.Pricing and top-ups
Mistral Medium 3.1 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-medium-3.1",
"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-medium-3.1",
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-medium-3.1",
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 Medium 3.1 good for?
Mistral publishes this model; we expose it under the id "mistralai/mistral-medium-3.1". Its context window is 131,072 tokens, roughly 98k English words in one request. A single response can run to 104,857 tokens. On price it sits in the "cheap" band — cheaper than 200 and dearer than 206 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 116,050 toman per 1M input tokens and 580,250 per 1M output tokens. A 1,000-word round trip on Mistral Medium 3.1 lands near 905 toman. It supports cached input: repeated context is billed at 11,605 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 Seed 1.6: Mistral Medium 3.1 works out roughly 1.1× more expensive, 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 110 thousand-word requests on Mistral Medium 3.1, and every 1,000 toman is about 1,105 words of round trip. A job with one million input tokens and one million output tokens comes to 696,300 toman in total. Filling this model's 131,072-token window costs 15,211 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. Both land at nearly the same price on a thousand-word request (905 and 905 toman).
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 Aion-2.0 at 131,072 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.
Mistral Medium 3.1 is an updated version of Mistral Medium 3, which is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances...
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 | 406 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 812 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 25,531 toman |
Frequently asked
- How do I call Mistral Medium 3.1 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-medium-3.1". Nothing else in your code changes.
- What does Mistral Medium 3.1 cost in toman?
- 116,050 toman per 1M input tokens and 580,250 toman per 1M output tokens; a 1,000-word request is around 905 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Mistral Medium 3.1 take?
- Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 104,857 tokens.
- Does Mistral Medium 3.1 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.