Error 502: what to do when gemini-3.1-pro-preview-customtools 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.

gemini-3.1-pro-preview-customtools is served by Google. 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 gemini-3.1-pro-preview-customtools 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 = 'gemini-3.1-pro-preview-customtools'


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
VendorGoogle
Context1M
CapabilitiesReasoning, Tools, Files, Vision, Audio
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 2 + cr * 0.2 + c * 12) : tier("200k_plus", p * 4 + cr * 0.4 + c * 18

FAQ

Does caching results reduce 502?

Concurrency and timeouts are the real variables here, not the model itself. Lower the concurrency first — most throughput complaints disappear once you do. A 1M context means long inputs add noticeably to first-token latency. Use batching or a queue to smooth peaks — steadier than raising concurrency on the fly.

Should retries add random jitter?

You usually do not need to change business code, just the call cadence. Retrying is the most effective first step. With billing p * 2 + cr * 0.2 + c * 12) : tier("200k_plus", p * 4 + cr * 0.4 + c * 18, failed requests are not counted toward usage. Use exponential backoff for retryable errors and return immediately for the rest.

Does switching to streaming reduce 502?

This is a client-side configuration issue; nothing changes server-side. The capability tags are Reasoning, Tools, Files, Vision, Audio, and parameter ceilings follow from that capability set. Truncate or summarise long inputs — it noticeably reduces 502.

Is 502 more likely with direct frontend calls or backend proxying?

Keep a fallback model ready as well. This model from Google 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.

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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