Which is cheaper, Qwen3 Coder 480B A35B or Qwen2.5 Coder 32B Instruct?

Qwen3 Coder 480B A35B is the cheaper of the two: one 1,000-word request costs about 490 toman, about 1.3× cheaper than Qwen2.5 Coder 32B Instruct.

Which one has the larger context window?

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

Qwen3 Coder 480B A35B vs Qwen2.5 Coder 32B Instruct

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, Qwen3 Coder 480B A35B comes out about 1.3× cheaper.

Feature	Qwen3 Coder 480B A35B	Qwen2.5 Coder 32B Instruct
Provider	Qwen (Alibaba)	Qwen (Alibaba)
Model id	qwen/qwen3-coder	qwen/qwen-2.5-coder-32b-instruct
Context	262,144 tokens	32,768 tokens
Max output	65,536 tokens	29,491 tokens
Input / 1M tokens	87,038 toman	191,483 toman
Output / 1M tokens	290,125 toman	290,125 toman
≈ one 1,000-word request	490 toman	626 toman
Input caching	29,013 toman / 1M	No
Image input	No	No
File input	No	No
Tool calling	Yes	No
JSON output	Yes	No
Reasoning mode	No	No

Which one for what?

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

Qwen2.5 Coder 32B Instruct

Chat, summarising, and everyday text generation

626 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": "qwen/qwen3-coder",
    "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="qwen/qwen3-coder",
    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: "qwen/qwen3-coder",
  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' => 'qwen/qwen3-coder',
    '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:    "qwen/qwen3-coder",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "qwen/qwen-2.5-coder-32b-instruct"

More comparisons: all pairs · model rankings · full catalogue

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