Error 400: what to do when glm-5.1 fails

A 400 means the request itself is invalid — a missing field, a wrong type, or a parameter this model does not support. Retrying will not help; the request body must change.

400 Bad Request means the server refuses to process the request because the body itself is invalid. Unlike 5xx, a 400 will not resolve by retrying — you get the same response every time. The parameters must be fixed first.

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: A required field is missing (model or messages). The recommended first action is: Check the request body against the parameter table on this page.

Common causes

  • A required field is missing (model or messages)
  • A parameter has the wrong type
  • A parameter is not supported by this model
  • messages is malformed or role has an invalid value

How to fix

  • Check the request body against the parameter table on this page
  • Remove unsupported parameters and retry
  • Make sure messages is an array with valid role values
  • Start from a minimal request, then add parameters one by one

Retry with exponential backoff

The snippet below retries when glm-5.1 returns 400, 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

Do I need to change request parameters when glm-5.1 returns 400?

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.

How many retry attempts is reasonable?

You usually do not need to change business code, just the call cadence. Retrying is the most effective first step. With billing p * 1.4 + cr * 0.26 + cc * 0 + c * 4.4, 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.

Is 400 related to how glm-5.1 is billed?

No charge — only output actually produced counts toward usage. With billing p * 1.4 + cr * 0.26 + cc * 0 + c * 4.4, the cost of long output comes mostly from output tokens. When estimating cost from p * 1.4 + cr * 0.26 + cc * 0 + c * 4.4, include the retry budget.

Does 400 recover automatically, or does it need manual action?

This error is unrelated to model capability; it is a gateway-layer issue. The capability tags for this model are Reasoning, Tools, Open Weights. Decide account-level versus model-level first; the two need completely different handling.

Other errors on this model

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Data updated: 2026-10-11 22:40

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