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GLM 5.3 API: toman pricing and code

z-ai/glm-5.3

toolsreasoningjson
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Input · per 1M tokens
406,175 toman
Output · per 1M tokens
1,276,550 toman
One 1,000-word request ≈
2,188 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 75,433 toman / 1M.Pricing and top-ups

GLM 5.3 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", 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", messages=messages, tools=tools)
print(final.choices[0].message.content)

What is GLM 5.3 good for?

The id for GLM 5.3 in our API is "z-ai/glm-5.3", served from Z.ai (GLM). Context is 1,310,720 tokens; past that you have to summarise the history yourself. A single response can run to 943,718 tokens. On price it sits in the "mid-range" band — cheaper than 278 and dearer than 128 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 2,188 toman per 1,000-word exchange (406,175 in, 1,276,550 out, per 1M tokens). It supports cached input: repeated context is billed at 75,433 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 o3 Mini: GLM 5.3 works out roughly 1.1× more expensive, 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 46 thousand-word requests on GLM 5.3, and every 1,000 toman is about 457 words of round trip. A job with one million input tokens and one million output tokens comes to 1,682,725 toman in total. Filling this model's 1,310,720-token window costs 532,382 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-latest", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 1.3× dearer (2,188 against 1,750 toman).

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, parallel_tool_calls, reasoning_effort, repetition_penalty — 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.

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 out1,120 toman
Summarising a ten-page document4,000 in + 600 out2,391 toman
Classifying a thousand short rows120,000 in + 20,000 out74,272 toman

Price history

DateInput (toman/1M)Output (toman/1M)
2026-09-07406,1751,276,550
2026-09-06406,1751,276,550

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

Frequently asked

How do I call GLM 5.3 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". Nothing else in your code changes.
What does GLM 5.3 cost in toman?
406,175 toman per 1M input tokens and 1,276,550 toman per 1M output tokens; a 1,000-word request is around 2,188 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GLM 5.3 take?
Up to 1,310,720 tokens per request, roughly 983k words. A single answer can reach 943,718 tokens.
Does GLM 5.3 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.