Mistral Small 4 API: toman pricing and code
mistralai/mistral-small-2603
visiontoolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 4,352 toman / 1M.Pricing and top-ups
Mistral Small 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.
# 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-small-2603",
"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-small-2603",
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-small-2603",
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 Small 4 good for?
The id for Mistral Small 4 in our API is "mistralai/mistral-small-2603", served from Mistral. Context is 262,144 tokens; past that you have to summarise the history yourself. A single response can run to 209,715 tokens. On price it sits in the "very cheap" band — cheaper than 95 and dearer than 311 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 43,519 toman per 1M tokens and output at 174,075 — output costs 4× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 4,352 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 GPT-4o-mini: Mistral Small 4 works out roughly 1× 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 353 thousand-word requests on Mistral Small 4, and every 1,000 toman is about 3,534 words of round trip. A job with one million input tokens and one million output tokens comes to 217,594 toman in total. Filling this model's 262,144-token window costs 11,408 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 6× cheaper (283 against 1,697 toman). On parameters, this one takes top_k.
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. Among the less common parameters it accepts reasoning_effort, top_k — all through the standard request body. By context size the nearest option from another provider is Trinity Large Thinking at 262,144 tokens.
Where it makes sense: high-volume work such as classification, tagging and bulk summarising, 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.
Mistral Small 4 is the next major release in the Mistral Small family, unifying the capabilities of several flagship Mistral models into a single system. It combines strong reasoning from...
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 | 135 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 279 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 8,704 toman |
Frequently asked
- How do I call Mistral Small 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-small-2603". Nothing else in your code changes.
- What does Mistral Small 4 cost in toman?
- 43,519 toman per 1M input tokens and 174,075 toman per 1M output tokens; a 1,000-word request is around 283 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Mistral Small 4 take?
- Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 209,715 tokens.
- Does Mistral Small 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.