DeepSeek V3 vs Qwen3 Coder 480B A35B

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.

FeatureDeepSeek V3Qwen3 Coder 480B A35B
ProviderDeepSeekQwen (Alibaba)
Model iddeepseek/deepseek-chatqwen/qwen3-coder
Context163,840 tokens262,144 tokens
Max output16,000 tokens65,536 tokens
Input / 1M tokens89,117 toman103,866 toman
Output / 1M tokens356,156 toman346,219 toman
≈ one 1,000-word request579 toman585 toman
Input cachingNo34,622 toman / 1M
Image inputNoNo
File inputNoNo
Tool callingYesYes
JSON outputYesYes
Reasoning modeNoNo

Which one for what?

DeepSeek V3

  • Agents that call your own APIs and database
579 toman per 1,000 words · Model page

Qwen3 Coder 480B A35B

  • Whole documents or a codebase in a single request
  • Agents that call your own APIs and database
585 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": "deepseek/deepseek-chat",
    "messages": [{"role": "user", "content": "Introduce yourself."}],
    "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="deepseek/deepseek-chat",
    messages=[{"role": "user", "content": "Introduce yourself."}],
    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: "deepseek/deepseek-chat",
  messages: [{ role: "user", content: "Introduce yourself." }],
  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' => 'deepseek/deepseek-chat',
    'messages' => [['role' => 'user', 'content' => 'Introduce yourself.']],
]);
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:    "deepseek/deepseek-chat",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "qwen/qwen3-coder"

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Frequently asked

Which is cheaper, DeepSeek V3 or Qwen3 Coder 480B A35B?
DeepSeek V3 is the cheaper of the two: one 1,000-word request costs about 579 toman.
Which one has the larger context window?
Qwen3 Coder 480B A35B, with 262,144 tokens.
Can I use DeepSeek V3 and Qwen3 Coder 480B A35B with one API key?
Yes. An uttapen key reaches every model unless you gave it an allowed-models list, and each request is charged in toman to the same wallet; only the model value changes.

More comparisons: all pairs · model rankings · full catalogue

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