uttapen
Create an API key

GLM 4.7 Flash API: toman pricing and code

z-ai/glm-4.7-flash

toolsreasoningjson
Create a keyTry it
Input · per 1M tokens
17,408 toman
Output · per 1M tokens
116,050 toman
One 1,000-word request ≈
173 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 2,901 toman / 1M.Pricing and top-ups

GLM 4.7 Flash example: a multi-step problem with reasoning

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

from openai import OpenAI

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

resp = client.chat.completions.create(
    model="z-ai/glm-4.7-flash",
    messages=[{"role": "user", "content": (
        "A shop has 3 warehouses. A ships 120 orders a day, B ships 85 and C ships 40. "
        "If C closes and its load is split between A and B in proportion to their capacity, how many does each ship a day? "
        "Work through it step by step and give just the two numbers at the end."
    )}],
    reasoning_effort="medium",   # low | medium | high
)
print(resp.choices[0].message.content)
# reasoning tokens count as output tokens too:
print(resp.usage.completion_tokens_details)

What is GLM 4.7 Flash good for?

The id for GLM 4.7 Flash in our API is "z-ai/glm-4.7-flash", served from Z.ai (GLM). Context is 202,752 tokens; past that you have to summarise the history yourself. A single response can run to 16,384 tokens. On price it sits in the "very cheap" band — cheaper than 71 and dearer than 335 of the other paid models in the catalogue.

What you get on top of text in, text out: 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 17,408 toman per 1M tokens and output at 116,050 — output costs 6.7× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 2,901 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 V3.2: GLM 4.7 Flash works out roughly 1.5× 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 578 thousand-word requests on GLM 4.7 Flash, and every 1,000 toman is about 5,780 words of round trip. A job with one million input tokens and one million output tokens comes to 133,458 toman in total. Filling this model's 202,752-token window costs 3,529 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-4.7", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 4.7× cheaper (173 against 811 toman). The context windows differ too: 202,752 against 204,800 tokens. Maximum answer length differs as well: 16,384 against 131,072 tokens. On parameters, this one takes logprobs, top_logprobs while the other takes top_a.

Z.ai (GLM) has 17 models in our catalogue; the cheapest is GLM Flash Latest at 116 toman per thousand words and the dearest GLM 5.3 at 2,188. Among the less common parameters it accepts logit_bias, logprobs, min_p, 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.

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

As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding capabilities, long-horizon task planning,...

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 out73 toman
Summarising a ten-page document4,000 in + 600 out139 toman
Classifying a thousand short rows120,000 in + 20,000 out4,410 toman

Frequently asked

How do I call GLM 4.7 Flash 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-flash". Nothing else in your code changes.
What does GLM 4.7 Flash cost in toman?
17,408 toman per 1M input tokens and 116,050 toman per 1M output tokens; a 1,000-word request is around 173 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GLM 4.7 Flash take?
Up to 202,752 tokens per request, roughly 152k words. A single answer can reach 16,384 tokens.
Does GLM 4.7 Flash 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.