Which is cheaper, Sonar or DeepSeek V3 0324?

DeepSeek V3 0324 is the cheaper of the two: one 1,000-word request costs about 471 toman, about 1.6× cheaper than Sonar.

Which one has the larger context window?

DeepSeek V3 0324, with 163,840 tokens.

Sonar vs DeepSeek V3 0324

Both sit behind the same key and the same code on uttapen — the only thing that changes is the model string, so you can run each of them against your own workload without touching anything else. On cost, DeepSeek V3 0324 comes out about 1.6× cheaper.

Feature	Sonar	DeepSeek V3 0324
Provider	Perplexity	DeepSeek
Model id	perplexity/sonar	deepseek/deepseek-chat-v3-0324
Context	127,072 tokens	163,840 tokens
Max output	114,364 tokens	147,456 tokens
Input / 1M tokens	290,125 toman	72,531 toman
Output / 1M tokens	290,125 toman	290,125 toman
≈ one 1,000-word request	754 toman	471 toman
Input caching	No	No
Image input	Yes	No
File input	No	No
Tool calling	No	Yes
JSON output	No	Yes
Reasoning mode	No	No

Which one for what?

Sonar

Reading screenshots, invoices, and scanned forms

754 toman per 1,000 words · Model page

DeepSeek V3 0324

Agents that call your own APIs and database
High-volume work where cost per call decides

471 toman per 1,000 words · Model page

Run both with the same code

Swap the model value between the two ids and send the same request twice; what each answer cost comes back in the X-Uttapen-Cost-Toman header.

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": "Introduce yourself in one sentence."}],
    "stream": true
  }'

from openai import OpenAI

client = OpenAI(
    base_url="https://api.uttapen.ir/v1",
    api_key="sk-up-…",
)

stream = client.chat.completions.create(
    model="perplexity/sonar",
    messages=[{"role": "user", "content": "Introduce yourself in one sentence."}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.uttapen.ir/v1",
  apiKey: "sk-up-…",
});

const stream = await client.chat.completions.create({
  model: "perplexity/sonar",
  messages: [{ role: "user", content: "Introduce yourself in one sentence." }],
  stream: true,
});
for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}

<?php
// composer require openai-php/client guzzlehttp/guzzle
$client = OpenAI::factory()
    ->withBaseUri('https://api.uttapen.ir/v1')
    ->withApiKey('sk-up-…')
    ->make();

$result = $client->chat()->create([
    'model' => 'perplexity/sonar',
    'messages' => [['role' => 'user', 'content' => 'Introduce yourself in one sentence.']],
]);
echo $result->choices[0]->message->content;

package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go"
	"github.com/openai/openai-go/option"
)

func main() {
	client := openai.NewClient(
		option.WithBaseURL("https://api.uttapen.ir/v1"),
		option.WithAPIKey("sk-up-…"),
	)
	resp, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
		Model:    "perplexity/sonar",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "deepseek/deepseek-chat-v3-0324"

More comparisons: all pairs · model rankings · full catalogue

base_url = https://api.uttapen.ir/v1