Sonar API: toman pricing and code
perplexity/sonar
visionYou are billed for the usage the request actually reported. Prices follow the market. Web search: 1,450.63 toman / request.Pricing and top-ups
Sonar 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": "perplexity/sonar",
"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="perplexity/sonar",
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: "perplexity/sonar",
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 Sonar good for?
Perplexity publishes this model; we expose it under the id "perplexity/sonar". Its context window is 127,072 tokens, roughly 95k English words in one request. A single response can run to 114,364 tokens. On price it sits in the "cheap" band — cheaper than 135 and dearer than 271 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 754 toman per 1,000-word exchange (290,125 in, 290,125 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 Qwen2.5 VL 72B Instruct: Sonar 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 133 thousand-word requests on Sonar, and every 1,000 toman is about 1,326 words of round trip. A job with one million input tokens and one million output tokens comes to 580,250 toman in total. Filling this model's 127,072-token window costs 36,867 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "perplexity/sonar-pro", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 9× cheaper (754 against 6,789 toman). The context windows differ too: 127,072 against 200,000 tokens. Maximum answer length differs as well: 114,364 against 8,000 tokens.
Perplexity has 5 models in our catalogue; the cheapest is Sonar at 754 toman per thousand words and the dearest Sonar Pro Search at 6,789. Among the less common parameters it accepts top_k, web_search_options — 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.
Sonar is lightweight, affordable, fast, and simple to use — now featuring citations and the ability to customize sources. It is designed for companies seeking to integrate lightweight question-and-answer features...
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 | 551 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,335 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 40,618 toman |
Frequently asked
- How do I call Sonar 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 "perplexity/sonar". Nothing else in your code changes.
- What does Sonar cost in toman?
- 290,125 toman per 1M input tokens and 290,125 toman per 1M output tokens; a 1,000-word request is around 754 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Sonar take?
- Up to 127,072 tokens per request, roughly 95k words. A single answer can reach 114,364 tokens.
- Does Sonar 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.