GLM 4.5V API pricing in toman

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/glm-4.5v". Its context window is 65,536 tokens, roughly 49k English words in one request. A single response can run to 16,384 tokens. On price it sits in the "cheap" band — cheaper than 189 and dearer than 217 of the other paid models in the catalogue.

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 174,075 toman per 1M input tokens and 522,225 per 1M output tokens. A 1,000-word round trip on GLM 4.5V lands near 905 toman. It supports cached input: repeated context is billed at 31,914 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 roughly 1.1× 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 110 thousand-word requests on GLM 4.5V, and every 1,000 toman is about 1,105 words of round trip. A job with one million input tokens and one million output tokens comes to 696,300 toman in total. Filling this model's 65,536-token window costs 11,408 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/glm-4.6v", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 2× dearer (905 against 453 toman). The context windows differ too: 65,536 against 131,072 tokens. Maximum answer length differs as well: 16,384 against 32,768 tokens.

Z.ai (GLM) has 17 models in our catalogue; the cheapest is GLM Flash Latest at 116 toman per thousand words and the dearest GLM 5.3 at 2,188. Among the less common parameters it accepts 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.

Provider's own description

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,...

Job	Tokens	Cost
One chat turn with a medium history	1,500 in + 400 out	470 toman
Summarising a ten-page document	4,000 in + 600 out	1,010 toman
Classifying a thousand short rows	120,000 in + 20,000 out	31,334 toman

Job

Tokens

Cost

One chat turn with a medium history

1,500 in + 400 out

470 toman

Summarising a ten-page document

4,000 in + 600 out

1,010 toman

Classifying a thousand short rows

120,000 in + 20,000 out

31,334 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?

174,075 toman per 1M input tokens and 522,225 toman per 1M output tokens; a 1,000-word request is around 905 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.

GLM 4.5V API: toman pricing and code

GLM 4.5V example: reading an image into JSON

What is GLM 4.5V good for?

Three real jobs, priced on this model

Frequently asked