Which is cheaper, Qwen3 VL 8B Thinking or Qwen3.6 27B?

Qwen3 VL 8B Thinking is the cheaper of the two: one 1,000-word request costs about 860 toman.

Which one has the larger context window?

Qwen3.6 27B, with 262,144 tokens.

Qwen3 VL 8B Thinking vs Qwen3.6 27B

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 8B Thinking	Qwen3.6 27B
Provider	Qwen (Alibaba)	Qwen (Alibaba)
Model id	qwen/qwen3-vl-8b-thinking	qwen/qwen3.6-27b
Context	131,072 tokens	262,144 tokens
Max output	32,768 tokens	65,536 tokens
Input / 1M tokens	52,223 toman	87,038 toman
Output / 1M tokens	609,263 toman	580,250 toman
≈ one 1,000-word request	860 toman	867 toman
Input caching	No	8,704 toman / 1M
Image input	Yes	Yes
File input	No	No
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	Yes	Yes

Which one for what?

Qwen3 VL 8B Thinking

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

860 toman per 1,000 words · Model page

Qwen3.6 27B

Multi-step problems, maths, and tracking down a bug
Whole documents or a codebase in a single request
Reading screenshots, invoices, and scanned forms
Agents that call your own APIs and database

867 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-8b-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-8b-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-8b-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-8b-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-8b-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/qwen3.6-27b"

More comparisons: all pairs · model rankings · full catalogue

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