Error 400: what to do when llama-3.3-70b-instruct 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.

llama-3.3-70b-instruct is served by Meta. 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 llama-3.3-70b-instruct 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 = 'llama-3.3-70b-instruct'


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
VendorMeta
Context128K
CapabilitiesTools, Files, Open Weights
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 0 + c * 0

FAQ

Is 400 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 Tools, Files, Open Weights. 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 400 on llama-3.3-70b-instruct related to request body size?

The request parameters must change; retrying alone will not help. This is a client-side configuration issue; nothing changes server-side. The capability tags are Tools, Files, Open Weights, and parameter ceilings follow from that capability set. The 128K 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 400.

What information should I give support when reporting an issue?

Group the errors by time and node first; the pattern is usually obvious once you do. If it only happens in production, it is usually an environment difference, not the model. This model is served by Meta, 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.

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. This model has a 128K context window and comes from Meta. A 128K context means long inputs add noticeably to first-token latency. Start with low concurrency, watch it for a few minutes, then scale up.

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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