Error 429: what to do when glm-4.6 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-4.6 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-4.6 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-4.6'


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
Context204.8K
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

Is 429 on glm-4.6 related to my account quota?

It is mainly a quota matter, not a fault in the model itself. With billing p * 0.6 + cr * 0.11 + cc * 0 + c * 2.2, the cost of long output comes mostly from output tokens. With billing p * 0.6 + cr * 0.11 + cc * 0 + c * 2.2, failed requests are not counted toward usage. 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 429 on glm-4.6 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 204.8K 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.

The official SDK already retries — do I still need my own retry logic?

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. Set the retry ceiling to 3–5 attempts and add jitter. Use exponential backoff for retryable errors and return immediately for the rest.

Does caching results reduce 429?

Concurrency and timeouts are the real variables here, not the model itself. A 204.8K 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

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Data updated: 2026-10-11 15:15

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