Error 401: what to do when gpt-4o fails

A 401 means authentication failed: the request carries no API key, or the key is invalid, deleted or expired.

401 Unauthorized means the request carries no valid credential. The gateway could not identify the caller, so the request never reaches the upstream model. Checking the Authorization header and the key status is the only path forward.

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: The Authorization header is missing. The recommended first action is: Send the header as Authorization: Bearer followed by your key.

Common causes

  • The Authorization header is missing
  • The key is misspelled or has stray whitespace
  • The key was deleted or disabled on the Tokens page
  • A key from another platform was used by mistake

How to fix

  • Send the header as Authorization: Bearer followed by your key
  • Generate a fresh key on the Tokens page
  • Check that you are not using a key from another service
  • Verify the key with a minimal curl request

Retry with exponential backoff

The snippet below retries when gpt-4o returns 401, 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

TPS844.92
Avg latency2119 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

When gpt-4o returns 401, is the gateway or the upstream more likely at fault?

This error is unrelated to model capability; it is a gateway-layer issue. It is usually transient and recovers on its own. The capability tags for this model are Tools, Files, Vision. If it persists for several minutes, contact platform support to confirm upstream status. Decide account-level versus model-level first; the two need completely different handling.

Is 401 related to how gpt-4o is billed?

No charge — only output actually produced counts toward usage. Billing follows p * 2.5 + cr * 1.25 + c * 10, so no output means no charge. Input price is about $2.50 per million tokens. When estimating cost from p * 2.5 + cr * 1.25 + c * 10, include the retry budget.

Is incomplete output from gpt-4o the same thing as 401?

Group the errors by time and node first; the pattern is usually obvious once you do. Keep the request ID and the raw response body, otherwise nothing can be traced. This model is served by OpenAI, 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.

Does sharing one key across several services make 401 more likely?

Raising the plan ceiling or lowering the call rate both help. Input price is about $2.50 per million tokens. Check your balance and rate limits in the console before debugging code.

Other errors on this model

Other models with the same error

Data updated: 2026-10-11 13:20

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