Error 502: what to do when glm-5 fails

A 502 means the gateway received an invalid response from the upstream model service: the request reached the gateway, but the hop to the upstream failed.

502 Bad Gateway means the gateway received an invalid response from the upstream model service. The request reached the gateway; the failure happened on the hop from gateway to upstream, usually due to an upstream restart or a dropped connection.

glm-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: The upstream model service is unavailable or returned a malformed response. The recommended first action is: Retry once — 502s are usually transient.

Common causes

  • The upstream model service is unavailable or returned a malformed response
  • An upstream node is restarting
  • The gateway-to-upstream connection was interrupted
  • The model was temporarily taken offline

How to fix

  • Retry once — 502s are usually transient
  • Switch to an equivalent model from another vendor
  • If only one model keeps returning 502, its upstream is unhealthy
  • Try again later or contact us

Retry with exponential backoff

The snippet below retries when glm-5 returns 502, 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'


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
Context204.8K
CapabilitiesReasoning, Tools, Open Weights
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 1 + cr * 0.2 + cc * 0 + c * 3.2

FAQ

Exponential backoff or a fixed interval?

You usually do not need to change business code, just the call cadence. With billing p * 1 + cr * 0.2 + cc * 0 + c * 3.2, failed requests are not counted toward usage. Use exponential backoff for retryable errors and return immediately for the rest.

Is 502 on glm-5 related to request body size?

The request parameters must change; retrying alone will not help. This is a client-side configuration issue; nothing changes server-side. The capability tags are Reasoning, Tools, Open Weights, and parameter ceilings follow from that capability set. The 204.8K context window sets the maximum input per request; anything beyond it is rejected outright. Fail fast on parameter errors instead of spending retries on them.

Will I be charged when glm-5 returns 502?

No charge — only output actually produced counts toward usage. Billing follows p * 1 + cr * 0.2 + cc * 0 + c * 3.2, so no output means no charge. With billing p * 1 + cr * 0.2 + cc * 0 + c * 3.2, the cost of long output comes mostly from output tokens. Check your balance and rate limits in the console before debugging code. When estimating cost from p * 1 + cr * 0.2 + cc * 0 + c * 3.2, include the retry budget.

Does 502 on glm-5 depend on region or node?

Keep the request ID and the raw response body, otherwise nothing can be traced. 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.

Other errors on this model

Other models with the same error

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Data updated: 2026-10-10 15:40

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