Create an API key

GLM 5.3 FlashX API: toman pricing and code

z-ai/glm-5.3-flashx

Image inputTool callingReasoningJSON output
Create a keyTry it
Input · per 1M tokens
128,101 toman
Output · per 1M tokens
432,774 toman
One 1,000-word request ≈
729 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 25,966 toman per 1M tokens.Pricing and top-ups

GLM 5.3 FlashX 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-5.3-flashx",
    "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-5.3-flashx",
    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-5.3-flashx",
  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 5.3 FlashX good for?

Z.ai (GLM) publishes this model; we expose it under the id "z-ai/glm-5.3-flashx". Its context window is 1,048,576 tokens, roughly 786k English words in one request. A single response can run to 131,072 tokens. On price it sits in the "cheap" band — cheaper than 227 and dearer than 167 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 128,101 toman per 1M input tokens and 432,774 per 1M output tokens. A 1,000-word round trip on GLM 5.3 FlashX lands near 729 toman. It supports cached input: repeated context is billed at 25,966 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 Gemini 3.5 Flash Lite (batch): GLM 5.3 FlashX works out about 20% more expensive, and its context window is the same size. 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 137 thousand-word requests on GLM 5.3 FlashX, and every 1,000 toman is about 1,372 words of round trip. A job with one million input tokens and one million output tokens comes to 560,875 toman in total. Filling this model's 1,048,576-token window costs 134,324 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-5.3-flashx-legal", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (729 and 729 toman). On parameters the other takes structured_outputs.

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 reasoning_effort, top_k — all through the standard request body. It does not support seed, 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 DeepSeek V4 Flash 0423 at 1,048,576 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, analysing a long document or codebase in one request.

Provider's own description

GLM-5.3-FlashX is the high-speed variant of Z.ai's GLM-5.3-Flash, a native multimodal model delivering inference speeds of up to 200 tokens/s. Built on the same hybrid sparse and linear attention architecture...

Three real jobs, priced on this model

Each figure is derived from the prices above and moves when they do.

JobTokensCost
One chat turn with a medium history1,500 in + 400 out365 toman
Summarising a ten-page document4,000 in + 600 out772 toman
Classifying a thousand short rows120,000 in + 20,000 out24,028 toman

Price history

DateInput (toman/1M)Output (toman/1M)
4 Nov 2026128,101432,774
23 Sep 2026128,101432,774
19 Sep 2026128,101432,774

Every price change for this model is recorded. Toman figures use today's rate.

Frequently asked

How do I call GLM 5.3 FlashX 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-5.3-flashx". Nothing else in your code changes.
What does GLM 5.3 FlashX cost in toman?
128,101 toman per 1M input tokens and 432,774 toman per 1M output tokens; a 1,000-word request is around 729 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GLM 5.3 FlashX take?
Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 131,072 tokens.
Does GLM 5.3 FlashX 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.