Error 504: what to do when gpt-5.1 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.

gpt-5.1 is served by OpenAI. 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 gpt-5.1 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 = 'gpt-5.1'


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
VendorOpenAI
Context400K
CapabilitiesReasoning, Tools, Files, Vision
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 1.25 + cr * 0.125 + c * 10

FAQ

How long should the timeout be?

Concurrency and timeouts are the real variables here, not the model itself. Lower the concurrency first — most throughput complaints disappear once you do. This model has a 400K context window and comes from OpenAI. A 400K context means long inputs add noticeably to first-token latency. Start with low concurrency, watch it for a few minutes, then scale up. Use batching or a queue to smooth peaks — steadier than raising concurrency on the fly.

Will I be charged when gpt-5.1 returns 504?

No charge — only output actually produced counts toward usage. Only the output already produced is billed; the failed part is not. With billing p * 1.25 + cr * 0.125 + c * 10, failed requests are not counted toward usage. Check your balance and rate limits in the console before debugging code. When estimating cost from p * 1.25 + cr * 0.125 + c * 10, include the retry budget.

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

You usually do not need to change business code, just the call cadence. Retrying is the most effective first step. With billing p * 1.25 + cr * 0.125 + c * 10, 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.

504 keeps recurring on gpt-5.1 — 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, Files, Vision. 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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