Error 429: what to do when glm-4.7 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-4.7 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-4.7 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-4.7'


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-4.7 related to my account quota?

It is mainly a quota matter, not a fault in the model itself. Raising the plan ceiling or lowering the call rate both help. 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.

Does switching to streaming reduce 429?

The request parameters must change; retrying alone will not help. The capability tags are Reasoning, Tools, Open Weights, and parameter ceilings follow from that capability set. Truncate or summarise long inputs — it noticeably reduces 429.

Staging is fine but production returns 429 — what could differ?

Keep the request ID and the raw response body, otherwise nothing can be traced. 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. Reproduce it once in a staging environment with the same request body.

Do failed requests count against my rate limit quota?

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.

Other errors on this model

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Data updated: 2026-10-10 18:35

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