Error 400: what to do when glm-5.3-flash 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-5.3-flash 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-5.3-flash 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-5.3-flash'


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
Context1M
CapabilitiesReasoning, Tools, Files, Open Weights, Vision
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 0.15 + cr * 0.03 + cc * 0 + c * 0.5

FAQ

Does switching to streaming reduce 400?

The request parameters must change; retrying alone will not help. This is a client-side configuration issue; nothing changes server-side. The 1M 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. Truncate or summarise long inputs — it noticeably reduces 400.

Should concurrent requests be queued or rate-limited directly?

Concurrency and timeouts are the real variables here, not the model itself. Lower the concurrency first — most throughput complaints disappear once you do. A 1M context means long inputs add noticeably to first-token latency. Add a cache layer so repeated requests do not all hit the model. Use batching or a queue to smooth peaks — steadier than raising concurrency on the fly.

Will I be charged when glm-5.3-flash returns 400?

No charge — only output actually produced counts toward usage. Input price is about $0.15 per million tokens. Check your balance and rate limits in the console before debugging code. When estimating cost from p * 0.15 + cr * 0.03 + cc * 0 + c * 0.5, include the retry budget.

Can 400 cause data loss?

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, Files, Open Weights, Vision. If it persists for several minutes, contact platform support to confirm upstream status. Decide account-level versus model-level first; the two need completely different handling.

Other errors on this model

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Data updated: 2026-10-10 18:35

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