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GLM Flash Latest API: toman pricing and code

~z-ai/glm-flash-latest

This id is an alias that always points at the newest version of this model. Send a fixed version's id if answers must not change when a new version ships.

Image inputTool callingReasoningJSON output
Create a keyTry it
Input · per 1M tokens
9,867 toman
Output · per 1M tokens
31,956 toman
One 1,000-word request ≈
54 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 9,867 toman per 1M tokens.Pricing and top-ups

GLM Flash Latest 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-flash-latest",
    "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-flash-latest",
    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-flash-latest",
  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 Flash Latest good for?

GLM Flash Latest comes from Z.ai (GLM); in uttapen you reach it with the model id "~z-ai/glm-flash-latest". It accepts up to 1,048,576 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 "very cheap" band — cheaper than 387 and dearer than 11 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 9,867 toman per 1M tokens and output at 31,956 — output costs 3.2× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 9,867 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 Ministral 3 3B 2512: GLM Flash Latest works out about 41% cheaper, and its context window is larger. 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 1,852 thousand-word requests on GLM Flash Latest, and every 1,000 toman is about 18,519 words of round trip. A job with one million input tokens and one million output tokens comes to 41,823 toman in total. Filling this model's 1,048,576-token window costs 10,347 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-5.3-flash:batch", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 47% cheaper (54 against 104 toman). On parameters, this one takes parallel_tool_calls.

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, parallel_tool_calls, presence_penalty, reasoning_effort — all through the standard request body. By context size the nearest option from another provider is DeepSeek V4 Flash 0423 at 1,048,576 tokens.

Where it makes sense: high-volume work such as classification, tagging and bulk summarising, 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

This model always redirects to the latest model in the GLM Flash family.

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 out28 toman
Summarising a ten-page document4,000 in + 600 out59 toman
Classifying a thousand short rows120,000 in + 20,000 out1,823 toman

Price history

DateInput (toman/1M)Output (toman/1M)
5 Nov 20269,86731,956
5 Nov 20269,86729,290
4 Nov 20269,76332,441
4 Nov 20269,31334,483
31 Oct 20265,64351,206
31 Oct 20266,23248,540
31 Oct 20266,82142,862
31 Oct 20268,20539,538
31 Oct 20267,47842,862
31 Oct 20268,20539,538
31 Oct 20267,47842,862
31 Oct 20266,82145,839

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

Frequently asked

How do I call GLM Flash Latest 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-flash-latest". Nothing else in your code changes.
What does GLM Flash Latest cost in toman?
9,867 toman per 1M input tokens and 31,956 toman per 1M output tokens; a 1,000-word request is around 54 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GLM Flash Latest take?
Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 131,072 tokens.
Does GLM Flash Latest 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.