Error 429: what to do when glm-5.1 fails

A 429 means you hit a rate limit: the number of requests or tokens sent within a time window exceeded your plan quota.

429 Too Many Requests is a rate-limit response, not an error. It means the request itself is valid but exceeded the request rate or concurrency allowed by your current plan. Back off and retry within the window indicated by the response headers; the request body does not need to change.

glm-5.1 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: Too many concurrent requests. The recommended first action is: Limit concurrency and retry with exponential backoff.

Common causes

  • Too many concurrent requests
  • Quota of the current plan exhausted
  • Retrying immediately without backoff

How to fix

  • Limit concurrency and retry with exponential backoff
  • Switch to a plan with a larger quota
  • Cache repeated requests

Retry with exponential backoff

The snippet below retries when glm-5.1 returns 429, 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-5.1'


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
Context200K
CapabilitiesReasoning, Tools, Open Weights
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 1.4 + cr * 0.26 + cc * 0 + c * 4.4

FAQ

Could 429 be caused by a proxy or another network hop?

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 Z.AI, 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.

Does 429 affect other models under the same account?

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

Is 429 related to how glm-5.1 is billed?

Only the output already produced is billed; the failed part is not. With billing p * 1.4 + cr * 0.26 + cc * 0 + c * 4.4, the cost of long output comes mostly from output tokens. With billing p * 1.4 + cr * 0.26 + cc * 0 + c * 4.4, failed requests are not counted toward usage. Check your balance and rate limits in the console before debugging code. When estimating cost from p * 1.4 + cr * 0.26 + cc * 0 + c * 4.4, include the retry budget.

How long should the timeout be?

Lower the concurrency first — most throughput complaints disappear once you do. This model has a 200K context window and comes from Z.AI. A 200K context means long inputs add noticeably to first-token latency. Add a cache layer so repeated requests do not all hit the model.

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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