Error 403: what to do when grok-4.20-multi-agent-0309 fails

A 403 means you are authenticated but not allowed: the key is valid, yet it may not call this model or this group.

403 Forbidden means the identity was recognised but access is denied. The difference from 401: 401 asks who you are, 403 says you lack permission. Usually the model needs to be added to the allowed list or group bound to your key.

grok-4.20-multi-agent-0309 is served by xAI. 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: This key is not authorised for this model. The recommended first action is: Add this model to the key allowlist on the Tokens page.

Common causes

  • This key is not authorised for this model
  • The model is not in the group the key is bound to
  • The key has an IP allowlist that excludes your current IP
  • The model was retired or requires higher permissions

How to fix

  • Add this model to the key allowlist on the Tokens page
  • Confirm the key group includes this model
  • Review the IP allowlist settings
  • Switch to a model you are allowed to call

Retry with exponential backoff

The snippet below retries when grok-4.20-multi-agent-0309 returns 403, 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 = 'grok-4.20-multi-agent-0309'


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
VendorxAI
Context1M
CapabilitiesReasoning, Files, Vision
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 1.25 + cr * 0.2 + c * 2.5) : tier("200k_plus", p * 2.5 + cr * 0.4 + c * 5

FAQ

Can setting max_tokens too high trigger 403?

The request parameters must change; retrying alone will not help. This is a client-side configuration issue; nothing changes server-side. 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 403.

Do failed requests count against my rate limit quota?

No charge — only output actually produced counts toward usage. With billing p * 1.25 + cr * 0.2 + c * 2.5) : tier("200k_plus", p * 2.5 + cr * 0.4 + c * 5, the cost of long output comes mostly from output tokens. Check your balance and rate limits in the console before debugging code. When estimating cost from p * 1.25 + cr * 0.2 + c * 2.5) : tier("200k_plus", p * 2.5 + cr * 0.4 + c * 5, include the retry budget.

Can switching to another model work around 403 on grok-4.20-multi-agent-0309?

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 xAI has several upstream nodes the gateway can switch between. Put the model name in config, so switching upstreams needs no code change.

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 1M context window and comes from xAI. A 1M context means long inputs add noticeably to first-token latency. Add a cache layer so repeated requests do not all hit the model. Use batching or a queue to smooth peaks — steadier than raising concurrency on the fly.

Other errors on this model

Other models with the same error

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Data updated: 2026-10-10 12:10

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