Error 502: what to do when grok-4.20-0309-non-reasoning 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.

grok-4.20-0309-non-reasoning is served by xAI. 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 grok-4.20-0309-non-reasoning 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 = 'grok-4.20-0309-non-reasoning'


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
VendorxAI
Context1M
CapabilitiesTools, Files, Vision
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 1.25 + cr * 0.2 + c * 2.5) : tier("200k_plus", p * 2.5 + cr * 0.4 + c * 5

FAQ

Does switching to a smaller model reduce 502?

Building a fallback into the architecture is more reliable than patching errors one by one. This model from xAI has several upstream nodes the gateway can switch between. Put the model name in config, so switching upstreams needs no code change.

Is 502 on grok-4.20-0309-non-reasoning related to my account quota?

Raising the plan ceiling or lowering the call rate both help. Billing follows p * 1.25 + cr * 0.2 + c * 2.5) : tier("200k_plus", p * 2.5 + cr * 0.4 + c * 5, so no output means no charge. Check your balance and rate limits in the console before debugging code. When estimating cost from p * 1.25 + cr * 0.2 + c * 2.5) : tier("200k_plus", p * 2.5 + cr * 0.4 + c * 5, include the retry budget.

Could 502 be caused by a proxy or another network hop?

Group the errors by time and node first; the pattern is usually obvious once you do. If it only happens in production, it is usually an environment difference, not the model. This model is served by xAI, so upstream status follows the vendor’s own announcements. Log the request ID on every failure — it beats the status code when debugging.

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 1M context window and comes from xAI. A 1M context means long inputs add noticeably to first-token latency. For long outputs, raise the timeout to 60 seconds or more.

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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