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GLM 5.3 (batch) API: toman pricing and code

z-ai/glm-5.3:batch

Tool callingReasoningJSON output
Create a keyTry it
Input · per 1M tokens
155,799 toman
Output · per 1M tokens
692,439 toman
One 1,000-word request ≈
1,103 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 34,622 toman per 1M tokens.Pricing and top-ups

GLM 5.3 (batch) example: tool calling

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

from openai import OpenAI
import json

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

tools = [{
    "type": "function",
    "function": {
        "name": "check_stock",
        "description": "Returns the stock level of a product",
        "parameters": {
            "type": "object",
            "properties": {"sku": {"type": "string"}},
            "required": ["sku"],
        },
    },
}]

messages = [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}]
first = client.chat.completions.create(model="z-ai/glm-5.3:batch", messages=messages, tools=tools)
call = first.choices[0].message.tool_calls[0]

# you run the function yourself — the model never touches the database
args = json.loads(call.function.arguments)
result = {"sku": args["sku"], "qty": 7}

messages += [first.choices[0].message, {"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)}]
final = client.chat.completions.create(model="z-ai/glm-5.3:batch", messages=messages, tools=tools)
print(final.choices[0].message.content)
import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });

const tools = [{
  type: "function",
  function: {
    name: "check_stock",
    description: "Returns the stock level of a product",
    parameters: { type: "object", properties: { sku: { type: "string" } }, required: ["sku"] },
  },
}];

const messages = [{ role: "user", content: "How many of the Nike NK-42 shoe are in stock?" }];
const first = await client.chat.completions.create({ model: "z-ai/glm-5.3:batch", messages, tools });
const call = first.choices[0].message.tool_calls[0];

const args = JSON.parse(call.function.arguments);
const result = { sku: args.sku, qty: 7 };

messages.push(first.choices[0].message, { role: "tool", tool_call_id: call.id, content: JSON.stringify(result) });
const final = await client.chat.completions.create({ model: "z-ai/glm-5.3:batch", messages, tools });
console.log(final.choices[0].message.content);
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:batch",
    "messages": [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "check_stock",
        "parameters": {"type": "object", "properties": {"sku": {"type": "string"}}, "required": ["sku"]}
      }
    }]
  }'

What is GLM 5.3 (batch) good for?

GLM 5.3 (batch) comes from Z.ai (GLM); in uttapen you reach it with the model id "z-ai/glm-5.3:batch". It accepts up to 1,048,576 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 131,072 tokens. On price it sits in the "mid-range" band — cheaper than 185 and dearer than 200 of the other paid models in the catalogue.

Capabilities available on this id: 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.

For a back-of-envelope figure: about 1,103 toman per 1,000-word exchange (155,799 in, 692,439 out, per 1M tokens). It supports cached input: repeated context is billed at 34,622 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 Devstral 2 2512, and GLM 5.3 (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 91 thousand-word requests on GLM 5.3 (batch), and every 1,000 toman is about 907 words of round trip. A job with one million input tokens and one million output tokens comes to 848,237 toman in total. Filling this model's 1,048,576-token window costs 163,367 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", 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 70% more expensive (1,103 against 662 toman). Maximum answer length differs as well: 131,072 against 943,718 tokens. On parameters the other takes logprobs, parallel_tool_calls, top_logprobs.

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, min_p, presence_penalty, 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 0423 at 1,048,576 tokens.

What to use it for: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, 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 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context window, and improves...

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 out511 toman
Summarising a ten-page document4,000 in + 600 out1,039 toman
Classifying a thousand short rows120,000 in + 20,000 out32,545 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-5.3484,7071,048,576

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

Price history

DateInput (toman/1M)Output (toman/1M)
23 Sep 2026155,799692,439
22 Sep 2026249,278830,926
8 Sep 2026242,354761,683

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

Frequently asked

How do I call GLM 5.3 (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:batch". Nothing else in your code changes.
What does GLM 5.3 (batch) cost in toman?
155,799 toman per 1M input tokens and 692,439 toman per 1M output tokens; a 1,000-word request is around 1,103 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GLM 5.3 (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 (batch) support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Structured output through response_format works too.