Error 400: what to do when gpt-4o fails

A 400 means the request itself is invalid — a missing field, a wrong type, or a parameter this model does not support. Retrying will not help; the request body must change.

400 Bad Request means the server refuses to process the request because the body itself is invalid. Unlike 5xx, a 400 will not resolve by retrying — you get the same response every time. The parameters must be fixed first.

gpt-4o is served by OpenAI. 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: A required field is missing (model or messages). The recommended first action is: Check the request body against the parameter table on this page.

Common causes

  • A required field is missing (model or messages)
  • A parameter has the wrong type
  • A parameter is not supported by this model
  • messages is malformed or role has an invalid value

How to fix

  • Check the request body against the parameter table on this page
  • Remove unsupported parameters and retry
  • Make sure messages is an array with valid role values
  • Start from a minimal request, then add parameters one by one

Retry with exponential backoff

The snippet below retries when gpt-4o returns 400, 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 = 'gpt-4o'


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

TPS1027.03
Avg latency1414 ms
Success rate100%
API endpointhttps://api.airai.cc/v1
OpenAI-compatibleOpenAI-compatible
VendorOpenAI
Context128K
CapabilitiesTools, Files, Vision
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 2.5 + cr * 1.25 + c * 10

FAQ

How long should the retry interval be when gpt-4o returns 400?

Retrying is the most effective first step. Measured success rate is 100%, so the first retry usually carries the highest marginal benefit. With billing p * 2.5 + cr * 1.25 + c * 10, failed requests are not counted toward usage. Set the retry ceiling to 3–5 attempts and add jitter. Use exponential backoff for retryable errors and return immediately for the rest.

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. Measured throughput is 1027.03, a useful ceiling for concurrency. Add a cache layer so repeated requests do not all hit the model.

Do failed requests count against my rate limit quota?

No charge — only output actually produced counts toward usage. With billing p * 2.5 + cr * 1.25 + c * 10, failed requests are not counted toward usage. Check your balance and rate limits in the console before debugging code. When estimating cost from p * 2.5 + cr * 1.25 + c * 10, include the retry budget.

Is 400 related to the capability of the model itself?

This error is unrelated to model capability; it is a gateway-layer issue. The capability tags for this model are Tools, Files, Vision. At a 100% success rate, an occasional 400 is normal variation. Decide account-level versus model-level first; the two need completely different handling.

Other errors on this model

Other models with the same error

Data updated: 2026-10-10 18:35

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