Error 400: what to do when gemini-3.1-flash-lite 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.

gemini-3.1-flash-lite 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: 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 gemini-3.1-flash-lite 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 = 'gemini-3.1-flash-lite'


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

TPS373.24
Avg latency2186 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

Is 400 on gemini-3.1-flash-lite related to my account quota?

It is mainly a quota matter, not a fault in the model itself. Input price is about $0.25 per million tokens. Check your balance and rate limits in the console before debugging code.

Staging is fine but production returns 400 — what could differ?

Keep the request ID and the raw response body, otherwise nothing can be traced. If it only happens in production, it is usually an environment difference, not the model. The 100% 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.

Can 400 cause data loss?

This error is unrelated to model capability; it is a gateway-layer issue. At a 100% success rate, an occasional 400 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.

Will I be charged when gemini-3.1-flash-lite returns 400?

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 $0.25 per million tokens. With billing p * 0.25 + cr * 0.025 + ai * 0.5 + c * 1.5, failed requests are not counted toward usage. Check your balance and rate limits in the console before debugging code. When estimating cost from p * 0.25 + cr * 0.025 + ai * 0.5 + c * 1.5, include the retry budget.

Other errors on this model

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Data updated: 2026-10-10 18:35

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