Sonar Pro API: toman pricing and code
perplexity/sonar-pro
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 Pro 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-pro",
"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-pro",
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-pro",
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 Pro good for?
Sonar Pro is one of Perplexity's models. Put "perplexity/sonar-pro" in the model field and the rest of your code stays as it is. Sonar Pro keeps 200,000 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 8,000 tokens. On price it sits in the "expensive" band — cheaper than 359 and dearer than 47 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. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.
Input runs at 870,375 toman per 1M tokens and output at 4,351,875 — output costs 5× 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 GPT-5.4 Image 2: Sonar Pro works out roughly 1.3× cheaper, 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 15 thousand-word requests on Sonar Pro, and every 1,000 toman is about 147 words of round trip. A job with one million input tokens and one million output tokens comes to 5,222,250 toman in total. Filling this model's 200,000-token window costs 174,075 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", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 9× dearer (6,789 against 754 toman). The context windows differ too: 200,000 against 127,072 tokens. Maximum answer length differs as well: 8,000 against 114,364 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 Claude 3 Haiku at 200,000 tokens.
Where it makes sense: work where the quality of the answer matters more than its cost, pulling text and fields out of images, analysing a long document or codebase in one request.
Note: Sonar Pro pricing includes Perplexity search pricing. See [details here](https://docs.perplexity.ai/guides/pricing#detailed-pricing-breakdown-for-sonar-reasoning-pro-and-sonar-pro) For enterprises seeking more advanced capabilities, the Sonar Pro API can handle in-depth, multi-step queries with added extensibility, like...
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 | 3,046 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 6,093 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 191,483 toman |
Frequently asked
- How do I call Sonar Pro 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-pro". Nothing else in your code changes.
- What does Sonar Pro cost in toman?
- 870,375 toman per 1M input tokens and 4,351,875 toman per 1M output tokens; a 1,000-word request is around 6,789 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Sonar Pro take?
- Up to 200,000 tokens per request, roughly 150k words. A single answer can reach 8,000 tokens.
- Does Sonar Pro 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.