Which is cheaper, GLM 4.6V or GLM 4.5 Air?

GLM 4.5 Air is the cheaper of the two: one 1,000-word request costs about 370 toman, about 1.2× cheaper than GLM 4.6V.

GLM 4.6V vs GLM 4.5 Air

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. On cost, GLM 4.5 Air comes out about 1.2× cheaper.

Feature	GLM 4.6V	GLM 4.5 Air
Provider	Z.ai (GLM)	Z.ai (GLM)
Model id	z-ai/glm-4.6v	z-ai/glm-4.5-air
Context	131,072 tokens	131,072 tokens
Max output	32,768 tokens	98,304 tokens
Input / 1M tokens	87,038 toman	37,716 toman
Output / 1M tokens	261,113 toman	246,606 toman
≈ one 1,000-word request	453 toman	370 toman
Input caching	15,957 toman / 1M	7,253 toman / 1M
Image input	Yes	No
File input	No	No
Tool calling	Yes	Yes
JSON output	Yes	No
Reasoning mode	Yes	Yes

Which one for what?

GLM 4.6V

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

453 toman per 1,000 words · Model page

GLM 4.5 Air

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

370 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-4.6v",
    "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-4.6v",
    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-4.6v",
  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-4.6v',
    '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-4.6v",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "z-ai/glm-4.5-air"

More comparisons: all pairs · model rankings · full catalogue

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