A lightweight GLM for high-volume inference: from GLM 4.5 Air to GLM 5.3 Flash

For a long time the everyday lightweight Z.AI model was GLM 4.5 Air. In the Yenisei catalog it has been replaced by GLM 5.3 Flash (glm-5.3-flash) and GLM 4.7 Flash (glm-4.7-flash). Their job is the same: process a stream of similar requests cheaply and quickly.

Where it belongs

• Classification. Label tickets by category, detect sentiment, sort incoming messages. A lightweight model copes with this and costs pennies.
• Routing. Use it as a "gatekeeper": quickly tell whether a question is hard or simple and decide whether to pass it on to the flagship.
• Short answers. An FAQ bot, auto-replies, extracting fields from text.

Why this pays off

A typical mistake is to push all traffic through the senior model "just to be safe". It works in a demo, but in a production system the bill doubles for nothing. 80% of requests in a real product are simple, and a lightweight model is enough for them.

from openai import OpenAI
client = OpenAI(base_url="https://api.yenisei.ru/v1", api_key="YENISEI_KEY")
resp = client.chat.completions.create(
    model="glm-5.3-flash",
    messages=[
        {"role": "system", "content": "Return only the category: billing, tech, sales."},
        {"role": "user", "content": "My card payment is not going through."},
    ],
)
print(resp.choices[0].message.content)  # billing

Honest about the weak spots

Complex reasoning is beyond a lightweight model. Do not hand it multi-step logic, subtle refactoring, or analysis of a large document with conclusions: it will start cutting corners. That is what GLM 5.2 (glm-5.2) and GLM 5.3 (glm-5.3) are for.

The right approach is a cascade: the lightweight model filters and handles the simple cases, and the hard ones go up the chain. That way you pay for the expensive model only where it is actually needed.