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GLM 4.7 API: toman pricing and code

z-ai/glm-4.7

Tool callingReasoningJSON output
Create a keyTry it
Input · per 1M tokens
207,732 toman
Output · per 1M tokens
761,683 toman
One 1,000-word request ≈
1,260 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 38,084 toman per 1M tokens.Pricing and top-ups

GLM 4.7 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-4.7", 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-4.7", 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-4.7", 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-4.7", 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-4.7",
    "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 4.7 good for?

GLM 4.7 is one of Z.ai (GLM)'s models. Put "z-ai/glm-4.7" in the model field and the rest of your code stays as it is. GLM 4.7 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 179 and dearer than 217 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 1,260 toman per 1,000-word exchange (207,732 in, 761,683 out, per 1M tokens). It supports cached input: repeated context is billed at 38,084 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, and GLM 4.7 costs about the same, 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 79 thousand-word requests on GLM 4.7, and every 1,000 toman is about 794 words of round trip. A job with one million input tokens and one million output tokens comes to 969,414 toman in total. Filling this model's 204,800-token window costs 42,543 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. Both land at nearly the same price on a thousand-word request (1,260 and 1,125 toman). On parameters, this one takes top_a.

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, repetition_penalty, top_a, top_k — 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-4.7 is Z.ai’s latest flagship model, featuring upgrades in two key areas: enhanced programming capabilities and more stable multi-step reasoning/execution. It demonstrates significant improvements in executing complex agent tasks while...

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 out616 toman
Summarising a ten-page document4,000 in + 600 out1,288 toman
Classifying a thousand short rows120,000 in + 20,000 out40,161 toman

Price history

DateInput (toman/1M)Output (toman/1M)
4 Nov 2026207,732761,683
18 Oct 2026186,958685,514
17 Oct 2026207,732761,683
17 Oct 2026186,958685,514
25 Sep 2026207,732761,683
7 Sep 2026138,488605,884

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

Frequently asked

How do I call GLM 4.7 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-4.7". Nothing else in your code changes.
What does GLM 4.7 cost in toman?
207,732 toman per 1M input tokens and 761,683 toman per 1M output tokens; a 1,000-word request is around 1,260 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GLM 4.7 take?
Up to 204,800 tokens per request, roughly 154k words. A single answer can reach 131,072 tokens.
Does GLM 4.7 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.