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GLM 5.3 Flash (batch) API: toman pricing and code

z-ai/glm-5.3-flash:batch

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
17,311 toman
Output · per 1M tokens
62,319 toman
One 1,000-word request ≈
104 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 3,462 toman per 1M tokens.Pricing and top-ups

GLM 5.3 Flash (batch) 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:batch",
    "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:batch",
    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:batch",
  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 (batch) good for?

Z.ai (GLM) publishes this model; we expose it under the id "z-ai/glm-5.3-flash:batch". 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 "very cheap" band — cheaper than 364 and dearer than 32 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 17,311 toman per 1M tokens and output at 62,319 — output costs 3.6× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 3,462 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 Ling 3.0 Flash VL, and GLM 5.3 Flash (batch) costs about the same, 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 962 thousand-word requests on GLM 5.3 Flash (batch), and every 1,000 toman is about 9,615 words of round trip. A job with one million input tokens and one million output tokens comes to 79,630 toman in total. Filling this model's 1,048,576-token window costs 18,152 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", because the underlying model is the same. The difference is the suffix: "batch" against the standard variant. On a thousand-word request this variant works out about 2.8× cheaper (104 against 293 toman). Maximum answer length differs as well: 131,072 against 943,717 tokens. On parameters the other takes parallel_tool_calls.

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 logit_bias, logprobs, min_p, presence_penalty, reasoning_effort, repetition_penalty — all through the standard request body. By context size the nearest option from another provider is DeepSeek V4 Flash 0423 at 1,048,576 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.

Provider's own description

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.

JobTokensCost
One chat turn with a medium history1,500 in + 400 out51 toman
Summarising a ten-page document4,000 in + 600 out107 toman
Classifying a thousand short rows120,000 in + 20,000 out3,324 toman

Other variants of this model

Same core model, different execution terms and different price. This page is the batch variant.

VariantModel idOutput / 1MContext
standardz-ai/glm-5.3-flash173,1101,048,576

Put the variant's id verbatim in the model field; nothing else in your code changes.

Price history

DateInput (toman/1M)Output (toman/1M)
16 Oct 202617,31162,319
22 Sep 202620,77369,244
8 Sep 202625,96686,555
7 Sep 202651,933173,110

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

Frequently asked

How do I call GLM 5.3 Flash (batch) 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:batch". Nothing else in your code changes.
What does GLM 5.3 Flash (batch) cost in toman?
17,311 toman per 1M input tokens and 62,319 toman per 1M output tokens; a 1,000-word request is around 104 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GLM 5.3 Flash (batch) take?
Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 131,072 tokens.
Does GLM 5.3 Flash (batch) 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.