Error 504: what to do when claude-sonnet-4-5-20250929 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.

claude-sonnet-4-5-20250929 is served by Anthropic. 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 claude-sonnet-4-5-20250929 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 = 'claude-sonnet-4-5-20250929'


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
VendorAnthropic
Context1M
CapabilitiesReasoning, Tools, Files, Vision
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 3 + cr * 0.3 + cc * 3.75 + cc1h * 6 + c * 15

FAQ

Is 504 related to how claude-sonnet-4-5-20250929 is billed?

No charge — only output actually produced counts toward usage. Billing follows p * 3 + cr * 0.3 + cc * 3.75 + cc1h * 6 + c * 15, so no output means no charge. With billing p * 3 + cr * 0.3 + cc * 3.75 + cc1h * 6 + c * 15, the cost of long output comes mostly from output tokens. Check your balance and rate limits in the console before debugging code.

The official SDK already retries — do I still need my own retry logic?

You usually do not need to change business code, just the call cadence. Retrying is the most effective first step. With billing p * 3 + cr * 0.3 + cc * 3.75 + cc1h * 6 + c * 15, failed requests are not counted toward usage. Set the retry ceiling to 3–5 attempts and add jitter.

Is 504 more likely with direct frontend calls or backend proxying?

Keep a fallback model ready as well. This model from Anthropic has several upstream nodes the gateway can switch between. Keep a lighter fallback model ready so the main flow never breaks. Put the model name in config, so switching upstreams needs no code change.

Does a long context window make 504 more likely?

This is a client-side configuration issue; nothing changes server-side. The capability tags are Reasoning, Tools, Files, Vision, and parameter ceilings follow from that capability set. The 1M context window sets the maximum input per request; anything beyond it is rejected outright. Truncate or summarise long inputs — it noticeably reduces 504.

Other errors on this model

Other models with the same error

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Data updated: 2026-10-10 15:45

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