Error 401: what to do when glm-4.5-air 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.

glm-4.5-air is served by Z.AI. 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 glm-4.5-air 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 = 'glm-4.5-air'


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
VendorZ.AI
Context131.1K
CapabilitiesReasoning, Tools, Open Weights
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 0.2 + cr * 0.03 + cc * 0 + c * 1.1

FAQ

Is 401 more likely with direct frontend calls or backend proxying?

Keep a fallback model ready as well. This model from Z.AI 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.

Do I need to upgrade my plan to fix 401 on glm-4.5-air?

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 $0.20 per million tokens. With billing p * 0.2 + cr * 0.03 + cc * 0 + c * 1.1, failed requests are not counted toward usage. When estimating cost from p * 0.2 + cr * 0.03 + cc * 0 + c * 1.1, include the retry budget.

Do I need to change request parameters when glm-4.5-air returns 401?

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 Reasoning, Tools, Open Weights, and parameter ceilings follow from that capability set. Fail fast on parameter errors instead of spending retries on them. Truncate or summarise long inputs — it noticeably reduces 401.

Should concurrent requests be queued or rate-limited directly?

Concurrency and timeouts are the real variables here, not the model itself. This model has a 131.1K context window and comes from Z.AI. A 131.1K context means long inputs add noticeably to first-token latency. For long outputs, raise the timeout to 60 seconds or more.

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Data updated: 2026-10-10 15:55

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