Which is cheaper, Qwen3 VL 8B Instruct or Qwen3 8B?

Qwen3 VL 8B Instruct is the cheaper of the two: one 1,000-word request costs about 216 toman.

Which one has the larger context window?

Qwen3 VL 8B Instruct, with 262,144 tokens.

Qwen3 VL 8B Instruct vs Qwen3 8B

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 Instruct	Qwen3 8B
Provider	Qwen (Alibaba)	Qwen (Alibaba)
Model id	qwen/qwen3-vl-8b-instruct	qwen/qwen3-8b
Context	262,144 tokens	131,072 tokens
Max output	32,768 tokens	8,192 tokens
Input / 1M tokens	33,945 toman	33,945 toman
Output / 1M tokens	132,007 toman	132,007 toman
≈ one 1,000-word request	216 toman	216 toman
Input caching	No	No
Image input	Yes	No
File input	No	No
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	No	Yes

Which one for what?

Qwen3 VL 8B Instruct

Whole documents or a codebase in a single request
Reading screenshots, invoices, and scanned forms
Agents that call your own APIs and database
High-volume work where cost per call decides

216 toman per 1,000 words · Model page

Qwen3 8B

Multi-step problems, maths, and tracking down a bug
Agents that call your own APIs and database
High-volume work where cost per call decides

216 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-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/qwen3-vl-8b-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/qwen3-vl-8b-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/qwen3-vl-8b-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/qwen3-vl-8b-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/qwen3-8b"

More comparisons: all pairs · model rankings · full catalogue

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