Error 401: what to do when glm-4.5 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 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 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'


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 * 0.6 + cr * 0.11 + cc * 0 + c * 2.2

FAQ

How much concurrency is safe?

Lower the concurrency first — most throughput complaints disappear once you do. 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. Use batching or a queue to smooth peaks — steadier than raising concurrency on the fly.

Is 401 related to how glm-4.5 is billed?

No charge — only output actually produced counts toward usage. Only the output already produced is billed; the failed part is not. Billing follows p * 0.6 + cr * 0.11 + cc * 0 + c * 2.2, so no output means no charge. With billing p * 0.6 + cr * 0.11 + cc * 0 + c * 2.2, 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 * 0.6 + cr * 0.11 + cc * 0 + c * 2.2, include the retry budget.

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

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 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.

Should retries add random jitter?

You usually do not need to change business code, just the call cadence. Retrying is the most effective first step. With billing p * 0.6 + cr * 0.11 + cc * 0 + c * 2.2, failed requests are not counted toward usage. Use exponential backoff for retryable errors and return immediately for the rest.

Other errors on this model

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Data updated: 2026-10-10 15:45

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