Sonar Reasoning Pro API: toman pricing and code
perplexity/sonar-reasoning-pro
visionreasoningYou 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 Reasoning 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-reasoning-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-reasoning-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-reasoning-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 Reasoning Pro good for?
Sonar Reasoning Pro comes from Perplexity; in uttapen you reach it with the model id "perplexity/sonar-reasoning-pro". It accepts up to 128,000 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 115,200 tokens. On price it sits in the "mid-range" band — cheaper than 317 and dearer than 89 of the other paid models in the catalogue.
What it can do beyond plain text: it reads images directly, which makes it a real option for invoices, forms and screenshots; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.
Pricing is 580,250 toman per 1M input tokens and 2,321,000 per 1M output tokens. A 1,000-word round trip on Sonar Reasoning Pro lands near 3,772 toman. 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 Image: Sonar Reasoning Pro works out roughly 2× 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 27 thousand-word requests on Sonar Reasoning Pro, and every 1,000 toman is about 265 words of round trip. A job with one million input tokens and one million output tokens comes to 2,901,250 toman in total. Filling this model's 128,000-token window costs 74,272 toman on the input side alone, which is the real reason to keep conversation history short.
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 response_format, tool_choice, tools, seed, 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.
Good fits: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, pulling text and fields out of images.
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) Sonar Reasoning Pro is a premier reasoning model powered by DeepSeek R1 with Chain of Thought (CoT). Designed for...
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 | 1,799 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 3,714 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 116,050 toman |
Frequently asked
- How do I call Sonar Reasoning 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-reasoning-pro". Nothing else in your code changes.
- What does Sonar Reasoning Pro cost in toman?
- 580,250 toman per 1M input tokens and 2,321,000 toman per 1M output tokens; a 1,000-word request is around 3,772 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Sonar Reasoning Pro take?
- Up to 128,000 tokens per request, roughly 96k words. A single answer can reach 115,200 tokens.
- Does Sonar Reasoning 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.