Error 502: what to do when MiniMax-M3 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.

MiniMax-M3 is served by MiniMax. 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 MiniMax-M3 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 = 'MiniMax-M3'


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

TPS107.19
Avg latency8279 ms
Success rate100%
API endpointhttps://api.airai.cc/v1
OpenAI-compatibleOpenAI-compatible
VendorMiniMax
Context1M
CapabilitiesReasoning, Tools, Files, Open Weights, Vision
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 0.3 + cr * 0.06 + c * 1.2) : tier("512k_plus", p * 0.6 + cr * 0.12 + c * 2.4

FAQ

Will I be charged when MiniMax-M3 returns 502?

No charge — only output actually produced counts toward usage. Only the output already produced is billed; the failed part is not. Input price is about $0.30 per million tokens. Check your balance and rate limits in the console before debugging code. When estimating cost from p * 0.3 + cr * 0.06 + c * 1.2) : tier("512k_plus", p * 0.6 + cr * 0.12 + c * 2.4, include the retry budget.

How long should 502 on MiniMax-M3 persist before I contact support?

If it only happens in production, it is usually an environment difference, not the model. Measured success rate is 100%, and most failures surface as 502. This model is served by MiniMax, so upstream status follows the vendor’s own announcements. 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.

Can setting max_tokens too high trigger 502?

The request parameters must change; retrying alone will not help. The capability tags are Reasoning, Tools, Files, Open Weights, 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 502.

How long should the timeout be?

Lower the concurrency first — most throughput complaints disappear once you do. This model has a 1M context window and comes from MiniMax. Measured throughput is 107.19, a useful ceiling for concurrency. Add a cache layer so repeated requests do not all hit the model. Use batching or a queue to smooth peaks — steadier than raising concurrency on the fly.

Other errors on this model

Other models with the same error

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Data updated: 2026-10-10 18:35

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