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GLM 5.1 API: toman pricing and code

z-ai/glm-5.1

Tool callingReasoningJSON output
Create a keyTry it
Input · per 1M tokens
484,707 toman
Output · per 1M tokens
1,523,365 toman
One 1,000-word request ≈
2,610 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 90,017 toman per 1M tokens.Pricing and top-ups

GLM 5.1 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-5.1",
    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))
import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });

const resp = await client.chat.completions.create({
  model: "z-ai/glm-5.1",
  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: {
      name: "ticket",
      strict: true,
      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,
      },
    },
  },
});
console.log(JSON.parse(resp.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-5.1",
    "messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
    "response_format": {"type": "json_object"}
  }'

What is GLM 5.1 good for?

GLM 5.1 comes from Z.ai (GLM); in uttapen you reach it with the model id "z-ai/glm-5.1". It accepts up to 204,800 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 131,072 tokens. On price it sits in the "mid-range" band — cheaper than 125 and dearer than 273 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 484,707 toman per 1M input tokens and 1,523,365 per 1M output tokens. A 1,000-word round trip on GLM 5.1 lands near 2,610 toman. It supports cached input: repeated context is billed at 90,017 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 Qwen3.7 Max, and GLM 5.1 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 38 thousand-word requests on GLM 5.1, and every 1,000 toman is about 383 words of round trip. A job with one million input tokens and one million output tokens comes to 2,008,072 toman in total. Filling this model's 204,800-token window costs 99,268 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. Both land at nearly the same price on a thousand-word request (2,610 and 2,340 toman). The context windows differ too: 204,800 against 202,752 tokens. On parameters, this one takes frequency_penalty, logit_bias, logprobs, min_p.

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, logprobs, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is MiniMax M2 at 204,800 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, analysing a long document or codebase in one request.

Provider's own description

GLM-5.1 delivers a major leap in coding capability, with particularly significant gains in handling long-horizon tasks. Unlike previous models built around minute-level interactions, GLM-5.1 can work independently and continuously on...

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 out1,336 toman
Summarising a ten-page document4,000 in + 600 out2,853 toman
Classifying a thousand short rows120,000 in + 20,000 out88,632 toman

Price history

DateInput (toman/1M)Output (toman/1M)
28 Oct 2026484,7071,523,365
27 Oct 2026334,4481,051,122
21 Oct 2026484,7071,523,365
15 Oct 2026334,4481,051,122
14 Oct 2026460,4721,447,197
7 Oct 2026334,4481,051,122
3 Oct 2026484,7071,523,365
30 Sep 2026333,9631,049,599
30 Sep 2026484,7071,523,365
29 Sep 2026333,9631,049,599
29 Sep 2026484,7071,523,365
28 Sep 2026333,9631,049,599

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

Frequently asked

How do I call GLM 5.1 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-5.1". Nothing else in your code changes.
What does GLM 5.1 cost in toman?
484,707 toman per 1M input tokens and 1,523,365 toman per 1M output tokens; a 1,000-word request is around 2,610 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GLM 5.1 take?
Up to 204,800 tokens per request, roughly 154k words. A single answer can reach 131,072 tokens.
Does GLM 5.1 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.