Mistral Small 3.1 24B API: toman pricing and code
mistralai/mistral-small-3.1-24b-instruct
visionYou 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/mistral-small-3.1-24b-instruct" in the model field and the rest of your code stays as it is. Mistral Small 3.1 24B keeps 128,000 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 102,400 tokens. On price it sits in the "very cheap" band — cheaper than 93 and dearer than 313 of the other paid models in the catalogue.
Capabilities available on this id: it reads images directly, which makes it a real option for invoices, forms and screenshots. 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 342 toman per 1,000-word exchange (101,834 in, 161,019 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 UI-TARS 7B : Mistral Small 3.1 24B works out roughly 3× 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 292 thousand-word requests on Mistral Small 3.1 24B, and every 1,000 toman is about 2,924 words of round trip. A job with one million input tokens and one million output tokens comes to 262,853 toman in total. Filling this model's 128,000-token window costs 13,035 toman on the input side alone, which is the real reason to keep conversation history short.
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 logit_bias, logprobs, min_p, repetition_penalty, top_k, top_logprobs — all through the standard request body. It does not support include_reasoning, reasoning, response_format, tool_choice, 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.
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 | 217 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 504 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 15,440 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?
- 101,834 toman per 1M input tokens and 161,019 toman per 1M output tokens; a 1,000-word request is around 342 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 image input?
- Streaming (stream=true) works on every model here. Image input is accepted through image_url, as a data URI or a public URL.