Which is cheaper, Qwen3 VL 235B A22B Thinking or Qwen3 Max?

Qwen3 VL 235B A22B Thinking is the cheaper of the two: one 1,000-word request costs about 1,660 toman.

Which one has the larger context window?

Qwen3 Max, with 262,144 tokens.

Qwen3 VL 235B A22B Thinking vs Qwen3 Max

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	Qwen3 VL 235B A22B Thinking	Qwen3 Max
Provider	Qwen (Alibaba)	Qwen (Alibaba)
Model id	qwen/qwen3-vl-235b-a22b-thinking	qwen/qwen3-max
Context	131,072 tokens	262,144 tokens
Max output	32,768 tokens	65,536 tokens
Input / 1M tokens	116,050 toman	226,298 toman
Output / 1M tokens	1,160,500 toman	1,131,488 toman
≈ one 1,000-word request	1,660 toman	1,765 toman
Input caching	No	45,260 toman / 1M
Image input	Yes	No
File input	No	No
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	Yes	No

Which one for what?

Qwen3 VL 235B A22B Thinking

Multi-step problems, maths, and tracking down a bug
Reading screenshots, invoices, and scanned forms
Agents that call your own APIs and database

1,660 toman per 1,000 words · Model page

Qwen3 Max

Whole documents or a codebase in a single request
Agents that call your own APIs and database

1,765 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-vl-235b-a22b-thinking",
    "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-vl-235b-a22b-thinking",
    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-vl-235b-a22b-thinking",
  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-vl-235b-a22b-thinking',
    '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-vl-235b-a22b-thinking",
		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-max"

More comparisons: all pairs · model rankings · full catalogue

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