Which is cheaper, Qwen3.8 2.4T A95B or Qwen3.8 2.4T A95B (batch)?

Qwen3.8 2.4T A95B is the cheaper of the two: one 1,000-word request costs about 3,017 toman.

Which one has the larger context window?

Qwen3.8 2.4T A95B, with 1,048,576 tokens.

Qwen3.8 2.4T A95B vs Qwen3.8 2.4T A95B (batch)

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	Qwen3.8 2.4T A95B	Qwen3.8 2.4T A95B (batch)
Provider	Qwen (Alibaba)	Qwen (Alibaba)
Model id	qwen/qwen3.8-2.4t-a95b	qwen/qwen3.8-2.4t-a95b:batch
Context	1,048,576 tokens	1,010,000 tokens
Max output	262,144 tokens	909,000 tokens
Input / 1M tokens	580,250 toman	580,250 toman
Output / 1M tokens	1,740,750 toman	1,740,750 toman
≈ one 1,000-word request	3,017 toman	3,017 toman
Input caching	72,531 toman / 1M	72,531 toman / 1M
Image input	No	No
File input	No	No
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	Yes	Yes

Which one for what?

Qwen3.8 2.4T A95B

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

3,017 toman per 1,000 words · Model page

Qwen3.8 2.4T A95B (batch)

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

3,017 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/qwen3.8-2.4t-a95b",
    "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/qwen3.8-2.4t-a95b",
    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/qwen3.8-2.4t-a95b",
  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/qwen3.8-2.4t-a95b',
    '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/qwen3.8-2.4t-a95b",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "qwen/qwen3.8-2.4t-a95b:batch"

More comparisons: all pairs · model rankings · full catalogue

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