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GLM 5 API: toman pricing and code

z-ai/glm-5

toolsreasoningjson
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Input · per 1M tokens
174,075 toman
Output · per 1M tokens
557,040 toman
One 1,000-word request ≈
950 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 34,815 toman / 1M.Pricing and top-ups

GLM 5 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", 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", messages=messages, tools=tools)
print(final.choices[0].message.content)

What is GLM 5 good for?

GLM 5 is one of Z.ai (GLM)'s models. Put "z-ai/glm-5" in the model field and the rest of your code stays as it is. GLM 5 keeps 204,800 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 128,000 tokens. On price it sits in the "cheap" band — cheaper than 198 and dearer than 208 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 950 toman per 1,000-word exchange (174,075 in, 557,040 out, per 1M tokens). It supports cached input: repeated context is billed at 34,815 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 DeepSeek V4 Pro 0423: GLM 5 works out roughly 1.1× cheaper, 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 105 thousand-word requests on GLM 5, and every 1,000 toman is about 1,053 words of round trip. A job with one million input tokens and one million output tokens comes to 731,115 toman in total. Filling this model's 204,800-token window costs 35,651 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-4.6", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 1.2× dearer (950 against 822 toman). Maximum answer length differs as well: 128,000 against 16,384 tokens. On parameters, this one takes logprobs, top_logprobs.

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, repetition_penalty, top_k, top_logprobs — all through the standard request body. By context size the nearest option from another provider is MiniMax M2 at 204,800 tokens.

What to use it for: high-volume work such as classification, tagging and bulk summarising, 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 is Z.ai’s flagship open-source foundation model engineered for complex systems design and long-horizon agent workflows. Built for expert developers, it delivers production-grade performance on large-scale programming tasks, rivaling leading...

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 out484 toman
Summarising a ten-page document4,000 in + 600 out1,031 toman
Classifying a thousand short rows120,000 in + 20,000 out32,030 toman

Frequently asked

How do I call GLM 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-5". Nothing else in your code changes.
What does GLM 5 cost in toman?
174,075 toman per 1M input tokens and 557,040 toman per 1M output tokens; a 1,000-word request is around 950 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GLM 5 take?
Up to 204,800 tokens per request, roughly 154k words. A single answer can reach 128,000 tokens.
Does GLM 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.