Error 401: what to do when qwen-turbo fails

A 401 means authentication failed: the request carries no API key, or the key is invalid, deleted or expired.

401 Unauthorized means the request carries no valid credential. The gateway could not identify the caller, so the request never reaches the upstream model. Checking the Authorization header and the key status is the only path forward.

qwen-turbo is served by Alibaba. 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: The Authorization header is missing. The recommended first action is: Send the header as Authorization: Bearer followed by your key.

Common causes

  • The Authorization header is missing
  • The key is misspelled or has stray whitespace
  • The key was deleted or disabled on the Tokens page
  • A key from another platform was used by mistake

How to fix

  • Send the header as Authorization: Bearer followed by your key
  • Generate a fresh key on the Tokens page
  • Check that you are not using a key from another service
  • Verify the key with a minimal curl request

Retry with exponential backoff

The snippet below retries when qwen-turbo returns 401, 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 = 'qwen-turbo'


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
VendorAlibaba
Context1M
CapabilitiesReasoning, Tools
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 0.05 + c * 0.5) : tier("standard", p * 0.05 + c * 0.2

FAQ

How should I use the Retry-After header in the response?

You usually do not need to change business code, just the call cadence. With billing p * 0.05 + c * 0.5) : tier("standard", p * 0.05 + c * 0.2, failed requests are not counted toward usage. Use exponential backoff for retryable errors and return immediately for the rest.

Is 401 on qwen-turbo 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 * 0.05 + c * 0.5) : tier("standard", p * 0.05 + c * 0.2, the cost of long output comes mostly from output tokens. With billing p * 0.05 + c * 0.5) : tier("standard", p * 0.05 + c * 0.2, failed requests are not counted toward usage. Check your balance and rate limits in the console before debugging code. When estimating cost from p * 0.05 + c * 0.5) : tier("standard", p * 0.05 + c * 0.2, include the retry budget.

401 keeps recurring on qwen-turbo — how do I tell whether it is the model or my 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. 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.

Does switching to streaming reduce 401?

The request parameters must change; retrying alone will not help. The capability tags are Reasoning, Tools, and parameter ceilings follow from that capability set. Fail fast on parameter errors instead of spending retries on them.

Other errors on this model

Other models with the same error

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Data updated: 2026-10-10 15:50

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