Mistral Small 3.1 24B API: toman pricing and code
mistralai/mistral-small-3.1-24b-instruct
You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Mistral Small 3.1 24B 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-3.1-24b-instruct",
"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-3.1-24b-instruct",
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-3.1-24b-instruct",
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 3.1 24B good for?
Mistral Small 3.1 24B is one of Mistral's models. Put "mistralai/
Capabilities available on this id: 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. 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 408 toman per 1,000-word exchange (121,523 in, 192,152 out, per 1M tokens). 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 3.1 24B works out about 20% more expensive, 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 245 thousand-word requests on Mistral Small 3.1 24B, and every 1,000 toman is about 2,451 words of round trip. A job with one million input tokens and one million output tokens comes to 313,675 toman in total. Filling this model's 128,000-token window costs 15,555 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 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 logit_bias, logprobs, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. It does not support include_reasoning, reasoning, response_format, 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: 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 Small 3.1 24B Instruct is an upgraded variant of Mistral Small 3 (2501), featuring 24 billion parameters with advanced multimodal capabilities. It provides state-of-the-art performance in text-based reasoning and...
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 | 259 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 601 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 18,426 toman |
Frequently asked
- How do I call Mistral Small 3.1 24B 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-3.1-24b-instruct". Nothing else in your code changes. - What does Mistral Small 3.1 24B cost in toman?
- 121,523 toman per 1M input tokens and 192,152 toman per 1M output tokens; a 1,000-word request is around 408 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Mistral Small 3.1 24B take?
- Up to 128,000 tokens per request, roughly 96k words. A single answer can reach 102,400 tokens.
- Does Mistral Small 3.1 24B 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.