Error 503: what to do when glm-5.3 fails

A 503 means the service is temporarily unavailable, usually due to maintenance or overload. Unlike 429, it is not about your quota — it is a capacity problem on the server side.

503 Service Unavailable means the service cannot handle the request right now, usually from overload or maintenance. The distinction from 429 matters: 429 means your quota is used up, 503 means server capacity is short. Increase the backoff interval rather than swapping keys.

glm-5.3 is served by Z.AI. Everything on this page — triggers, fixes and measured data — is compiled from the real runtime behaviour of this model at the gateway layer.

At this gateway, the most common trigger is: The upstream model is overloaded. The recommended first action is: Retry later with a longer backoff.

Common causes

• The upstream model is overloaded
• The service is under maintenance or rolling out
• The node in your region is unavailable
• A sudden traffic spike

How to fix

• Retry later with a longer backoff
• Switch to a less loaded equivalent model
• Avoid batch jobs during peak hours
• Watch our announcements

Retry with exponential backoff

The snippet below retries when glm-5.3 returns 503, up to 5 attempts, with an increasing wait plus random jitter so concurrent calls do not retry in lockstep. Read the base URL and API key from environment variables — never hardcode them.

import os, time, random
import requests

BASE  = os.getenv("OPENAI_BASE_URL")   # e.g. https://<your-gateway>/v1
KEY   = os.getenv("OPENAI_API_KEY")
MODEL = 'glm-5.3'


def chat(messages, retries=5):
    """Retry with exponential backoff + jitter."""
    for i in range(retries):
        try:
            r = requests.post(
                BASE + "/chat/completions",
                headers={"Authorization": "Bearer " + KEY},
                json={"model": MODEL, "messages": messages, "stream": True},
                timeout=60,
            )
            if r.status_code == 429 or r.status_code >= 500:
                time.sleep(min(2 ** i + random.uniform(0, 1), 30))
                continue
            r.raise_for_status()
            return r.json()
        except requests.exceptions.Timeout:
            time.sleep(min(2 ** i + random.uniform(0, 1), 30))
    raise RuntimeError("gave up after " + str(retries) + " retries")


print(chat([{"role": "user", "content": "hello"}]))

Key facts for this model

FAQ

Other errors on this model

• glm-5.3: error 429 — causes and fixes
• glm-5.3: error timeout — causes and fixes
• glm-5.3: error 500 — causes and fixes
• glm-5.3: error 502 — causes and fixes
• glm-5.3: error 504 — causes and fixes
• glm-5.3: error 401 — causes and fixes
• glm-5.3: error 403 — causes and fixes
• glm-5.3: error 400 — causes and fixes

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• glm-5
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• kimi-k3
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• mimo-v2.5
• gpt-4o
• claude-opus-4-6

Back to the model page

Data updated: 2026-10-10 15:40