Mistral Small 3 API: toman pricing and code
mistralai/mistral-small-24b-instruct-2501
You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Mistral Small 3 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/mistral-small-24b-instruct-2501",
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))import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const resp = await client.chat.completions.create({
model: "mistralai/mistral-small-24b-instruct-2501",
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: {
name: "ticket",
strict: true,
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,
},
},
},
});
console.log(JSON.parse(resp.choices[0].message.content));curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "mistralai/mistral-small-24b-instruct-2501",
"messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
"response_format": {"type": "json_object"}
}'What is Mistral Small 3 good for?
Mistral publishes this model; we expose it under the id "mistralai/
What you get on top of text in, text out: 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.
Input runs at 17,311 toman per 1M tokens and output at 27,698 — output costs 1.6× input, so trimming the answer saves more than trimming the prompt. 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 MythoMax 13B: Mistral Small 3 works out about 33% cheaper, 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 1,695 thousand-word requests on Mistral Small 3, and every 1,000 toman is about 16,949 words of round trip. A job with one million input tokens and one million output tokens comes to 45,009 toman in total. Filling this model's 32,768-token window costs 567 toman on the input side alone, which is the real reason to keep conversation history short.
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, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. It does not support include_reasoning, reasoning, tool_choice, tools, 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-RP 1.0 (8B) at 32,768 tokens.
Where it makes sense: high-volume work such as classification, tagging and bulk summarising, extracting data against a fixed schema.
Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions designed...
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 | 37 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 86 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 2,631 toman |
Frequently asked
- How do I call Mistral Small 3 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-24b-instruct-2501". Nothing else in your code changes. - What does Mistral Small 3 cost in toman?
- 17,311 toman per 1M input tokens and 27,698 toman per 1M output tokens; a 1,000-word request is around 59 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Mistral Small 3 take?
- Up to 32,768 tokens per request, roughly 25k words. A single answer can reach 16,384 tokens.
- Can I stream Mistral Small 3's output?
- Yes — with stream=true you get SSE events as the tokens are produced. This model has no tool calling and no image input, so pick a different one if you need either.