Error timeout: what to do when glm-5.2 fails

A timeout means the client gave up before the response arrived. Streaming LLM requests hit this most often, because the model thinks before the first token and that thinking phase produces no bytes.

A timeout is not an HTTP status code but a client-side wait limit. The request usually reached the gateway and was forwarded upstream, yet the model did not produce a result within the time your client allows. Long context, long output, and non-streaming calls are the three most common triggers.

glm-5.2 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 client timeout is set too low. The recommended first action is: Raise the client timeout to 300 seconds or more.

Common causes

• The client timeout is set too low
• A long prompt or context pushes first-token latency up
• The model is working on a long reasoning task
• Network jitter

How to fix

• Raise the client timeout to 300 seconds or more
• Enable streaming so you do not wait for the whole answer
• Shorten the context or use a faster model
• Lower max_tokens

Retry with exponential backoff

The snippet below retries when glm-5.2 returns timeout, 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-5.2'


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-5.2: error 429 — causes and fixes
• glm-5.2: error 500 — causes and fixes
• glm-5.2: error 502 — causes and fixes
• glm-5.2: error 503 — causes and fixes
• glm-5.2: error 504 — causes and fixes
• glm-5.2: error 401 — causes and fixes
• glm-5.2: error 403 — causes and fixes
• glm-5.2: 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

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Data updated: 2026-10-10 12:10