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 456 toman.

Which one has the larger context window?

Qwen3 Coder 480B A35B, with 262,144 tokens.

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.

Feature	DeepSeek V3	Qwen3 Coder 480B A35B
Provider	DeepSeek	Qwen (Alibaba)
Model id	deepseek/deepseek-chat	qwen/qwen3-coder
Context	163,840 tokens	262,144 tokens
Max output	16,384 tokens	65,536 tokens
Input / 1M tokens	92,840 toman	87,038 toman
Output / 1M tokens	258,211 toman	290,125 toman
≈ one 1,000-word request	456 toman	490 toman
Input caching	No	29,013 toman / 1M
Image input	No	No
File input	No	No
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	No	No

Which one for what?

DeepSeek V3

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

456 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
High-volume work where cost per call decides

490 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 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="deepseek/deepseek-chat",
    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: "deepseek/deepseek-chat",
  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' => 'deepseek/deepseek-chat',
    '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:    "deepseek/deepseek-chat",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "qwen/qwen3-coder"

More comparisons: all pairs · model rankings · full catalogue

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