Error timeout: what to do when gemini-3.1-pro-preview-customtools fails

A timeout means the client gave up before the response arrived. Streaming LLM requests hit this most often, because the model thinks before the first token and that thinking phase produces no bytes.

A timeout is not an HTTP status code but a client-side wait limit. The request usually reached the gateway and was forwarded upstream, yet the model did not produce a result within the time your client allows. Long context, long output, and non-streaming calls are the three most common triggers.

gemini-3.1-pro-preview-customtools 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 client timeout is set too low. The recommended first action is: Raise the client timeout to 300 seconds or more.

Common causes

  • The client timeout is set too low
  • A long prompt or context pushes first-token latency up
  • The model is working on a long reasoning task
  • Network jitter

How to fix

  • Raise the client timeout to 300 seconds or more
  • Enable streaming so you do not wait for the whole answer
  • Shorten the context or use a faster model
  • Lower max_tokens

Retry with exponential backoff

The snippet below retries when gemini-3.1-pro-preview-customtools returns timeout, 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-pro-preview-customtools'


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

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 * 2 + cr * 0.2 + c * 12) : tier("200k_plus", p * 4 + cr * 0.4 + c * 18

FAQ

Is timeout related to the capability of the model itself?

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. Decide account-level versus model-level first; the two need completely different handling.

Is timeout on gemini-3.1-pro-preview-customtools related to request body size?

The request parameters must change; retrying alone will not help. The capability tags are Reasoning, Tools, Files, Vision, Audio, and parameter ceilings follow from that capability set. The 1M context window sets the maximum input per request; anything beyond it is rejected outright. Fail fast on parameter errors instead of spending retries on them. Truncate or summarise long inputs — it noticeably reduces timeout.

Should retries add random jitter?

Retrying is the most effective first step. With billing p * 2 + cr * 0.2 + c * 12) : tier("200k_plus", p * 4 + cr * 0.4 + c * 18, 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.

Should I fall back to a backup model when timeout appears?

Keep a fallback model ready as well. 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. Put the model name in config, so switching upstreams needs no code change.

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Data updated: 2026-10-10 18:35

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