Which is cheaper, Qwen3.5 397B A17B or Qwen3 235B A22B Thinking 2507?

Qwen3 235B A22B Thinking 2507 is the cheaper of the two: one 1,000-word request costs about 954 toman.

Which one has the larger context window?

Qwen3.5 397B A17B, with 262,144 tokens.

Qwen3.5 397B A17B vs Qwen3 235B A22B Thinking 2507

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	Qwen3.5 397B A17B	Qwen3 235B A22B Thinking 2507
Provider	Qwen (Alibaba)	Qwen (Alibaba)
Model id	qwen/qwen3.5-397b-a17b	qwen/qwen3-235b-a22b-thinking-2507
Context	262,144 tokens	131,072 tokens
Max output	65,536 tokens	117,964 tokens
Input / 1M tokens	113,149 toman	66,729 toman
Output / 1M tokens	678,893 toman	667,288 toman
≈ one 1,000-word request	1,030 toman	954 toman
Input caching	No	No
Image input	Yes	No
File input	No	No
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	Yes	Yes

Which one for what?

Qwen3.5 397B A17B

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

1,030 toman per 1,000 words · Model page

Qwen3 235B A22B Thinking 2507

Multi-step problems, maths, and tracking down a bug
Agents that call your own APIs and database

954 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/qwen3.5-397b-a17b",
    "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/qwen3.5-397b-a17b",
    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/qwen3.5-397b-a17b",
  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/qwen3.5-397b-a17b',
    '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/qwen3.5-397b-a17b",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "qwen/qwen3-235b-a22b-thinking-2507"

More comparisons: all pairs · model rankings · full catalogue

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