Which is cheaper, Qwen2.5 Coder 32B Instruct or Qwen2.5 VL 72B Instruct?

Qwen2.5 Coder 32B Instruct is the cheaper of the two: one 1,000-word request costs about 626 toman.

Which one has the larger context window?

Qwen2.5 VL 72B Instruct, with 128,000 tokens.

Qwen2.5 Coder 32B Instruct vs Qwen2.5 VL 72B 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.

Feature	Qwen2.5 Coder 32B Instruct	Qwen2.5 VL 72B Instruct
Provider	Qwen (Alibaba)	Qwen (Alibaba)
Model id	qwen/qwen-2.5-coder-32b-instruct	qwen/qwen2.5-vl-72b-instruct
Context	32,768 tokens	128,000 tokens
Max output	29,491 tokens	115,200 tokens
Input / 1M tokens	191,483 toman	232,100 toman
Output / 1M tokens	290,125 toman	290,125 toman
≈ one 1,000-word request	626 toman	679 toman
Input caching	No	116,050 toman / 1M
Image input	No	Yes
File input	No	No
Tool calling	No	No
JSON output	No	Yes
Reasoning mode	No	No

Which one for what?

Qwen2.5 Coder 32B Instruct

Chat, summarising, and everyday text generation

626 toman per 1,000 words · Model page

Qwen2.5 VL 72B Instruct

Reading screenshots, invoices, and scanned forms

679 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/qwen-2.5-coder-32b-instruct",
    "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/qwen-2.5-coder-32b-instruct",
    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/qwen-2.5-coder-32b-instruct",
  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/qwen-2.5-coder-32b-instruct',
    '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/qwen-2.5-coder-32b-instruct",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "qwen/qwen2.5-vl-72b-instruct"

More comparisons: all pairs · model rankings · full catalogue

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