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GLM 5 API: toman pricing and code

z-ai/glm-5

Tool callingReasoningJSON output
Create a keyTry it
Input · per 1M tokens
346,219 toman
Output · per 1M tokens
1,107,902 toman
One 1,000-word request ≈
1,890 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 69,244 toman per 1M tokens.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)
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", 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", 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",
    "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 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 131,072 tokens. On price it sits in the "mid-range" band — cheaper than 144 and dearer than 249 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 1,890 toman per 1,000-word exchange (346,219 in, 1,107,902 out, per 1M tokens). It supports cached input: repeated context is billed at 69,244 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 about 9% 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 53 thousand-word requests on GLM 5, and every 1,000 toman is about 529 words of round trip. A job with one million input tokens and one million output tokens comes to 1,454,121 toman in total. Filling this model's 204,800-token window costs 70,906 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-turbo", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 17% cheaper (1,890 against 2,340 toman). The context windows differ too: 204,800 against 202,752 tokens. On parameters, this one takes frequency_penalty, logprobs, presence_penalty, repetition_penalty.

Z.ai (GLM) has 21 models in our catalogue; the cheapest is GLM 5.3 Flash at 57 toman per thousand words and the dearest GLM 5.3 Prime at 5,221. Among the less common parameters it accepts logprobs, presence_penalty, 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: 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 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 out962 toman
Summarising a ten-page document4,000 in + 600 out2,050 toman
Classifying a thousand short rows120,000 in + 20,000 out63,704 toman

Price history

DateInput (toman/1M)Output (toman/1M)
20 Oct 2026346,2191,107,902
20 Oct 2026207,732664,741
15 Oct 2026346,2191,107,902
7 Sep 2026207,732664,741

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

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?
346,219 toman per 1M input tokens and 1,107,902 toman per 1M output tokens; a 1,000-word request is around 1,890 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 131,072 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.