GLM 5.3 Flash API: toman pricing and code
z-ai/glm-5.3-flash
visiontoolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 4,352 toman / 1M.Pricing and top-ups
GLM 5.3 Flash 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-flash",
"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-flash",
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-flash",
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 Flash good for?
GLM 5.3 Flash is one of Z.ai (GLM)'s models. Put "z-ai/glm-5.3-flash" in the model field and the rest of your code stays as it is. GLM 5.3 Flash keeps 1,310,720 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 131,072 tokens. On price it sits in the "very cheap" band — cheaper than 51 and dearer than 355 of the other paid models in the catalogue.
What you get on top of text in, text out: 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.
Input runs at 21,759 toman per 1M tokens and output at 72,531 — output costs 3.3× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 4,352 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 Qwen3.5-9B (batch): GLM 5.3 Flash works out roughly 1.3× cheaper, and its context window is larger. 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 813 thousand-word requests on GLM 5.3 Flash, and every 1,000 toman is about 8,130 words of round trip. A job with one million input tokens and one million output tokens comes to 94,291 toman in total. Filling this model's 1,310,720-token window costs 28,520 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "z-ai/glm-5.3-flash:batch", because the underlying model is the same. The difference is the suffix: no suffix (the standard variant) against "batch". On a thousand-word request this variant works out 2× cheaper (123 against 245 toman). The context windows differ too: 1,310,720 against 1,048,575 tokens. Maximum answer length differs as well: 131,072 against 943,717 tokens. On parameters, this one takes seed.
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 logit_bias, logprobs, min_p, reasoning_effort, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is DeepSeek V4 Flash 0731 at 1,310,720 tokens.
Where it makes sense: 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.
GLM-5.3-Flash is a native multimodal model from Z.ai. It is suited for efficient coding and long-horizon agent tasks. Its hybrid sparse and linear attention architecture maintains accurate long-context behavior while...
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 | 62 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 131 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 4,062 toman |
Other variants of this model
Same core model, different execution terms and different price. This page is the standard variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| batch (cheaper, slower) | z-ai/glm-5.3-flash:batch | 145,063 | 1,048,575 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Frequently asked
- How do I call GLM 5.3 Flash 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-flash". Nothing else in your code changes.
- What does GLM 5.3 Flash cost in toman?
- 21,759 toman per 1M input tokens and 72,531 toman per 1M output tokens; a 1,000-word request is around 123 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does GLM 5.3 Flash take?
- Up to 1,310,720 tokens per request, roughly 983k words. A single answer can reach 131,072 tokens.
- Does GLM 5.3 Flash 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.