Which is cheaper, GLM 5.3 Flash (batch) or Qwen3 30B A3B?

Qwen3 30B A3B is the cheaper of the two: one 1,000-word request costs about 234 toman.

Which one has the larger context window?

GLM 5.3 Flash (batch), with 1,048,575 tokens.

GLM 5.3 Flash (batch) vs Qwen3 30B A3B

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	GLM 5.3 Flash (batch)	Qwen3 30B A3B
Provider	Z.ai (GLM)	Qwen (Alibaba)
Model id	z-ai/glm-5.3-flash:batch	qwen/qwen3-30b-a3b
Context	1,048,575 tokens	131,072 tokens
Max output	943,717 tokens	16,384 tokens
Input / 1M tokens	43,519 toman	34,815 toman
Output / 1M tokens	145,063 toman	145,063 toman
≈ one 1,000-word request	245 toman	234 toman
Input caching	8,704 toman / 1M	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?

GLM 5.3 Flash (batch)

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
High-volume work where cost per call decides

245 toman per 1,000 words · Model page

Qwen3 30B A3B

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

234 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": "z-ai/glm-5.3-flash:batch",
    "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="z-ai/glm-5.3-flash:batch",
    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: "z-ai/glm-5.3-flash:batch",
  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' => 'z-ai/glm-5.3-flash:batch',
    '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:    "z-ai/glm-5.3-flash:batch",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "qwen/qwen3-30b-a3b"

More comparisons: all pairs · model rankings · full catalogue

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