Error 504: what to do when glm-4.5 fails

A 504 means the gateway timed out waiting for the upstream model: the request was sent, but the upstream did not return within the gateway timeout.

504 Gateway Timeout means the gateway gave up waiting for the upstream response. The request was forwarded, but no result came back within the gateway limit. Shortening the output, switching to streaming, or lowering max_tokens usually fixes it.

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: The upstream took longer than the gateway timeout allows. The recommended first action is: Switch to streaming output.

Common causes

  • The upstream took longer than the gateway timeout allows
  • A long context pushed inference time up
  • An upstream node is stuck
  • A non-streaming request with a very long output

How to fix

  • Switch to streaming output
  • Shorten the context and lower max_tokens
  • Retry after a short wait
  • Use a model with faster inference

Retry with exponential backoff

The snippet below retries when glm-4.5 returns 504, 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

Can async or batch processing avoid 504?

Lower the concurrency first — most throughput complaints disappear once you do. 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.

Could 504 be caused by a proxy or another network hop?

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.

How long should the retry interval be when glm-4.5 returns 504?

You usually do not need to change business code, just the call cadence. Retrying is the most effective first step. 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. Use exponential backoff for retryable errors and return immediately for the rest.

Is 504 related to how glm-4.5 is billed?

Only the output already produced is billed; the failed part is not. With billing p * 0.6 + cr * 0.11 + cc * 0 + c * 2.2, 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 * 0.6 + cr * 0.11 + cc * 0 + c * 2.2, include the retry budget.

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Data updated: 2026-10-11 19:15

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