Create an API key

GLM 5V Turbo API: toman pricing and code

z-ai/glm-5v-turbo

Image inputTool callingReasoningJSON output
Create a keyTry it
Input · per 1M tokens
415,463 toman
Output · per 1M tokens
1,384,877 toman
One 1,000-word request ≈
2,340 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 83,093 toman per 1M tokens.Pricing and top-ups

GLM 5V Turbo example: reading an image into JSON

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

# tip: a data URI works too — base64 the file and prefix it with data:image/jpeg;base64,
curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "z-ai/glm-5v-turbo",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Return only the invoice number and the total, as JSON."},
        {"type": "image_url", "image_url": {"url": "https://example.com/factor.jpg"}}
      ]
    }]
  }'
import base64, json
from openai import OpenAI

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

img = base64.b64encode(open("factor.jpg", "rb").read()).decode()

resp = client.chat.completions.create(
    model="z-ai/glm-5v-turbo",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Return the invoice number, the date and the total as JSON."},
            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}},
        ],
    }],
    response_format={"type": "json_object"},
)
print(json.loads(resp.choices[0].message.content))
import OpenAI from "openai";
import { readFileSync } from "node:fs";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const img = readFileSync("factor.jpg").toString("base64");

const resp = await client.chat.completions.create({
  model: "z-ai/glm-5v-turbo",
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "Return the invoice number, the date and the total as JSON." },
      { type: "image_url", image_url: { url: `data:image/jpeg;base64,${img}` } },
    ],
  }],
  response_format: { type: "json_object" },
});
console.log(JSON.parse(resp.choices[0].message.content));

What is GLM 5V Turbo good for?

GLM 5V Turbo comes from Z.ai (GLM); in uttapen you reach it with the model id "z-ai/glm-5v-turbo". It accepts up to 202,752 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 132 and dearer than 263 of the other paid models in the catalogue.

What you get on top of text in, text out: it reads images directly, which makes it a real option for invoices, forms and screenshots; 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.

Input runs at 415,463 toman per 1M tokens and output at 1,384,877 — output costs 3.3× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 83,093 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 Inkling, and GLM 5V Turbo 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 43 thousand-word requests on GLM 5V Turbo, and every 1,000 toman is about 427 words of round trip. A job with one million input tokens and one million output tokens comes to 1,800,341 toman in total. Filling this model's 202,752-token window costs 84,236 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-4.6v", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 4.3× more expensive (2,340 against 540 toman). The context windows differ too: 202,752 against 131,072 tokens. Maximum answer length differs as well: 131,072 against 32,768 tokens. On parameters the other takes frequency_penalty, presence_penalty, repetition_penalty, seed.

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 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 MiniMax M2 at 204,800 tokens.

Where it makes sense: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, pulling text and fields out of images, 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-5V-Turbo is Z.ai’s first native multimodal agent foundation model, built for vision-based coding and agent-driven tasks. It natively handles image, video, and text inputs, excels at long-horizon planning, complex coding,...

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 out1,177 toman
Summarising a ten-page document4,000 in + 600 out2,493 toman
Classifying a thousand short rows120,000 in + 20,000 out77,553 toman

Frequently asked

How do I call GLM 5V Turbo 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-5v-turbo". Nothing else in your code changes.
What does GLM 5V Turbo cost in toman?
415,463 toman per 1M input tokens and 1,384,877 toman per 1M output tokens; a 1,000-word request is around 2,340 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GLM 5V Turbo take?
Up to 202,752 tokens per request, roughly 152k words. A single answer can reach 131,072 tokens.
Does GLM 5V Turbo support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.