GLM 4.5V API: toman pricing and code
z-ai/glm-4.5v
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 38,084 toman per 1M tokens.Pricing and top-ups
GLM 4.5V example: reading an image into JSON
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
# tip: a data URI works too — base64 the file and prefix it with data:image/jpeg;base64,
curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "z-ai/glm-4.5v",
"messages": [{
"role": "user",
"content": [
{"type": "text", "text": "Return only the invoice number and the total, as JSON."},
{"type": "image_url", "image_url": {"url": "https://example.com/factor.jpg"}}
]
}]
}'import base64, json
from openai import OpenAI
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
img = base64.b64encode(open("factor.jpg", "rb").read()).decode()
resp = client.chat.completions.create(
model="z-ai/glm-4.5v",
messages=[{
"role": "user",
"content": [
{"type": "text", "text": "Return the invoice number, the date and the total as JSON."},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}},
],
}],
response_format={"type": "json_object"},
)
print(json.loads(resp.choices[0].message.content))import OpenAI from "openai";
import { readFileSync } from "node:fs";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const img = readFileSync("factor.jpg").toString("base64");
const resp = await client.chat.completions.create({
model: "z-ai/glm-4.5v",
messages: [{
role: "user",
content: [
{ type: "text", text: "Return the invoice number, the date and the total as JSON." },
{ type: "image_url", image_url: { url: `data:image/jpeg;base64,${img}` } },
],
}],
response_format: { type: "json_object" },
});
console.log(JSON.parse(resp.choices[0].message.content));What is GLM 4.5V good for?
Z.ai (GLM) publishes this model; we expose it under the id "z-ai/
What it can do beyond plain text: it reads images directly, which makes it a real option for invoices, forms and screenshots; it supports tool calling, so it can invoke your own functions with valid arguments; it returns schema-valid JSON through response_format, ready to hand to your code; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.
Pricing is 207,732 toman per 1M input tokens and 623,195 per 1M output tokens. A 1,000-word round trip on GLM 4.5V lands near 1,080 toman. It supports cached input: repeated context is billed at 38,084 toman per 1M, which matters a lot if your system prompt is long. You are always charged for the usage the request actually reported, never for the estimate, and a failed request costs nothing.
The closest alternative with the same capabilities from a different provider is Qwen3.5 Plus 2026-04-20: GLM 4.5V works out about 10% more expensive, and its context window is smaller. Both run on the same key and the same code, so trying the other one is a single string change.
To make the figure concrete: 100,000 toman of credit buys roughly 93 thousand-word requests on GLM 4.5V, and every 1,000 toman is about 926 words of round trip. A job with one million input tokens and one million output tokens comes to 830,926 toman in total. Filling this model's 65,536-token window costs 13,614 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "z-ai/
Z.ai (GLM) has 21 models in our catalogue; the cheapest is GLM Flash Latest at 54 toman per thousand words and the dearest GLM 5.3 Prime at 5,221. Among the less common parameters it accepts presence_penalty, repetition_penalty, top_k — all through the standard request body. It does not support structured_outputs, which most models here do, so test before switching if your code relies on them. By context size the nearest option from another provider is Nano Banana Pro (Gemini 3 Pro Image Preview) at 65,536 tokens.
Good fits: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, pulling text and fields out of images, agents that reach out to APIs and databases, extracting data against a fixed schema.
GLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters, it achieves state-of-the-art results in video understanding,...
Three real jobs, priced on this model
Each figure is derived from the prices above and moves when they do.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 561 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,205 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 37,392 toman |
Frequently asked
- How do I call GLM 4.5V from Iran?
- Sign up with your mobile number, top the wallet up in toman, create an API key, then in the official OpenAI SDK point base_url at https:/
/ api.uttapen.ir/ v1 and set model to "z-ai/ glm-4.5v". Nothing else in your code changes. - What does GLM 4.5V cost in toman?
- 207,732 toman per 1M input tokens and 623,195 toman per 1M output tokens; a 1,000-word request is around 1,080 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does GLM 4.5V take?
- Up to 65,536 tokens per request, roughly 49k words. A single answer can reach 16,384 tokens.
- Does GLM 4.5V support streaming and tool calling?
- Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.