Error 502: what to do when deepseek-v4-pro 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.

deepseek-v4-pro is served by DeepSeek. 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 deepseek-v4-pro 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 = 'deepseek-v4-pro'


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

TPS0
Avg latency3683 ms
Success rate0%
API endpointhttps://api.airai.cc/v1
OpenAI-compatibleOpenAI-compatible
VendorDeepSeek
Context1M
CapabilitiesReasoning, Tools
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 1.32 + cr * 0.044 + c * 3.96) : tier("off_peak", p * 0.66 + cr * 0.022 + c * 1.98

FAQ

Can switching to another model work around 502 on deepseek-v4-pro?

Keep a fallback model ready as well. This model from DeepSeek has several upstream nodes the gateway can switch between. Put the model name in config, so switching upstreams needs no code change.

Will I be charged when deepseek-v4-pro returns 502?

Only the output already produced is billed; the failed part is not. Input price is about $1.32 per million tokens. When estimating cost from p * 1.32 + cr * 0.044 + c * 3.96) : tier("off_peak", p * 0.66 + cr * 0.022 + c * 1.98, include the retry budget.

Does switching to streaming reduce 502?

The request parameters must change; retrying alone will not help. This is a client-side configuration issue; nothing changes server-side. The capability tags are Reasoning, Tools, and parameter ceilings follow from that capability set. The 1M context window sets the maximum input per request; anything beyond it is rejected outright. Fail fast on parameter errors instead of spending retries on them. Truncate or summarise long inputs — it noticeably reduces 502.

What information should I give support when reporting an issue?

Keep the request ID and the raw response body, otherwise nothing can be traced. Measured success rate is 0%, and most failures surface as 502. The 0% success rate is averaged across nodes, so one weak node drags the whole figure down. 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.

Other errors on this model

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Data updated: 2026-10-10 15:40

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