Error 500: what to do when glm-4.5 fails

A 500 means the server hit an unexpected internal error while handling the request. It is usually not a problem with your request format, but a transient failure on the server or upstream model side.

500 Internal Server Error is an unexpected server-side failure. Most 500s are transient node faults and succeed on a retry with exponential backoff. If it persists, a specific parameter combination is likely hitting an unhandled edge case.

glm-4.5 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: A transient failure in the upstream model service. The recommended first action is: Retry after a short wait — most 500s are transient.

Common causes

  • A transient failure in the upstream model service
  • The request landed on a node that is restarting
  • The request body triggered an unhandled edge case
  • A momentary load spike

How to fix

  • Retry after a short wait — most 500s are transient
  • Try another model from the same vendor to see if it is model-specific
  • Simplify the request body (drop uncommon parameters) and retry
  • If it persists, contact us with the request time

Retry with exponential backoff

The snippet below retries when glm-4.5 returns 500, 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-4.5'


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

API endpointhttps://api.airai.cc/v1
OpenAI-compatibleOpenAI-compatible
VendorZ.AI
Context131.1K
CapabilitiesReasoning, Tools, Open Weights
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 0.6 + cr * 0.11 + cc * 0 + c * 2.2

FAQ

Why does glm-4.5 only return 500 during certain hours?

If it only happens in production, it is usually an environment difference, not the model. This model is served by Z.AI, so upstream status follows the vendor’s own announcements. Log the request ID on every failure — it beats the status code when debugging. Reproduce it once in a staging environment with the same request body.

Which status codes are worth retrying, and which never help?

You usually do not need to change business code, just the call cadence. With billing p * 0.6 + cr * 0.11 + cc * 0 + c * 2.2, failed requests are not counted toward usage. Set the retry ceiling to 3–5 attempts and add jitter.

How much concurrency is safe?

Concurrency and timeouts are the real variables here, not the model itself. This model has a 131.1K context window and comes from Z.AI. A 131.1K context means long inputs add noticeably to first-token latency. For long outputs, raise the timeout to 60 seconds or more. Start with low concurrency, watch it for a few minutes, then scale up.

500 keeps recurring on glm-4.5 — how do I tell whether it is the model or my account?

This error is unrelated to model capability; it is a gateway-layer issue. It is usually transient and recovers on its own. The capability tags for this model are Reasoning, Tools, Open Weights. If it persists for several minutes, contact platform support to confirm upstream status. Decide account-level versus model-level first; the two need completely different handling.

Other errors on this model

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Data updated: 2026-10-10 18:35

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