Error 401: what to do when gemini-3.1-flash-lite-preview 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.

gemini-3.1-flash-lite-preview is served by Google. 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 gemini-3.1-flash-lite-preview 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 = 'gemini-3.1-flash-lite-preview'


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

TPS437.37
Avg latency1194 ms
Success rate100%
API endpointhttps://api.airai.cc/v1
OpenAI-compatibleOpenAI-compatible
VendorGoogle
Context1M
CapabilitiesReasoning, Tools, Files, Vision, Audio
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 0.25 + cr * 0.025 + ai * 0.5 + c * 1.5

FAQ

Can switching to a comparable model from another vendor fix 401?

Building a fallback into the architecture is more reliable than patching errors one by one. This model from Google has several upstream nodes the gateway can switch between. Keep a lighter fallback model ready so the main flow never breaks.

When gemini-3.1-flash-lite-preview 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 Reasoning, Tools, Files, Vision, Audio. At a 100% success rate, an occasional 401 is normal variation. 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.

How long should the timeout be?

Lower the concurrency first — most throughput complaints disappear once you do. A 1M context means long inputs add noticeably to first-token latency. For long outputs, raise the timeout to 60 seconds or more. Use batching or a queue to smooth peaks — steadier than raising concurrency on the fly.

Is 401 on gemini-3.1-flash-lite-preview related to my account quota?

It is mainly a quota matter, not a fault in the model itself. Raising the plan ceiling or lowering the call rate both help. Input price is about $0.25 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

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Data updated: 2026-10-10 18:35

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