Error 401: what to do when kimi-k2.7-code 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.

kimi-k2.7-code is served by Moonshot 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: 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 kimi-k2.7-code 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 = 'kimi-k2.7-code'


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
VendorMoonshot AI
Context262.1K
CapabilitiesReasoning, Tools, Files, Open Weights, Vision
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 0.95 + cr * 0.19 + c * 4

FAQ

Does a long context window make 401 more likely?

The request parameters must change; retrying alone will not help. This is a client-side configuration issue; nothing changes server-side. The capability tags are Reasoning, Tools, Files, Open Weights, Vision, and parameter ceilings follow from that capability set. The 262.1K 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.

Is 401 more likely with direct frontend calls or backend proxying?

Keep a fallback model ready as well. Building a fallback into the architecture is more reliable than patching errors one by one. This model from Moonshot AI has several upstream nodes the gateway can switch between. Put the model name in config, so switching upstreams needs no code change.

Does caching results reduce 401?

Lower the concurrency first — most throughput complaints disappear once you do. This model has a 262.1K context window and comes from Moonshot AI. Start with low concurrency, watch it for a few minutes, then scale up. Use batching or a queue to smooth peaks — steadier than raising concurrency on the fly.

Is 401 related to how kimi-k2.7-code is billed?

No charge — only output actually produced counts toward usage. Only the output already produced is billed; the failed part is not. With billing p * 0.95 + cr * 0.19 + c * 4, failed requests are not counted toward usage. Check your balance and rate limits in the console before debugging code. When estimating cost from p * 0.95 + cr * 0.19 + c * 4, include the retry budget.

Other errors on this model

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Data updated: 2026-10-10 18:30

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