uttapen
Create an API key

GLM 4.5 API: toman pricing and code

z-ai/glm-4.5

toolsreasoningjson
Create a keyTry it
Input · per 1M tokens
174,075 toman
Output · per 1M tokens
638,275 toman
One 1,000-word request ≈
1,056 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 31,914 toman / 1M.Pricing and top-ups

GLM 4.5 example: JSON output against a schema

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-…")

schema = {
    "name": "ticket",
    "schema": {
        "type": "object",
        "properties": {
            "category": {"type": "string", "enum": ["fani", "mali", "forush"]},
            "priority": {"type": "integer", "minimum": 1, "maximum": 5},
            "summary": {"type": "string"},
        },
        "required": ["category", "priority", "summary"],
        "additionalProperties": False,
    },
    "strict": True,
}

resp = client.chat.completions.create(
    model="z-ai/glm-4.5",
    messages=[{"role": "user", "content": "Ticket: "For two days I cannot download my invoice and I was charged twice.""}],
    response_format={"type": "json_schema", "json_schema": schema},
)
print(json.loads(resp.choices[0].message.content))

What is GLM 4.5 good for?

Z.ai (GLM) publishes this model; we expose it under the id "z-ai/glm-4.5". Its context window is 131,072 tokens, roughly 98k English words in one request. A single response can run to 98,304 tokens. On price it sits in the "mid-range" band — cheaper than 215 and dearer than 191 of the other paid models in the catalogue.

What it can do beyond plain text: 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 174,075 toman per 1M input tokens and 638,275 per 1M output tokens. A 1,000-word round trip on GLM 4.5 lands near 1,056 toman. It supports cached input: repeated context is billed at 31,914 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 MiniMax M1: GLM 4.5 works out roughly 1× more expensive, and its context window is smaller. 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 95 thousand-word requests on GLM 4.5, and every 1,000 toman is about 947 words of round trip. A job with one million input tokens and one million output tokens comes to 812,350 toman in total. Filling this model's 131,072-token window costs 22,816 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", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (1,056 and 950 toman). The context windows differ too: 131,072 against 204,800 tokens. Maximum answer length differs as well: 98,304 against 128,000 tokens. On parameters the other takes frequency_penalty, logit_bias, logprobs, min_p.

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 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 Aion-2.0 at 131,072 tokens.

Good fits: 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.

Provider's own description

GLM-4.5 is our latest flagship foundation model, purpose-built for agent-based applications. It leverages a Mixture-of-Experts (MoE) architecture and supports a context length of up to 128k tokens. GLM-4.5 delivers significantly...

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 out516 toman
Summarising a ten-page document4,000 in + 600 out1,079 toman
Classifying a thousand short rows120,000 in + 20,000 out33,655 toman

Frequently asked

How do I call GLM 4.5 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-4.5". Nothing else in your code changes.
What does GLM 4.5 cost in toman?
174,075 toman per 1M input tokens and 638,275 toman per 1M output tokens; a 1,000-word request is around 1,056 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GLM 4.5 take?
Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 98,304 tokens.
Does GLM 4.5 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.