Error 400: what to do when kimi-k2.7-code 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.

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: 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 kimi-k2.7-code 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 = '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 sharing one key across several services make 400 more likely?

It is mainly a quota matter, not a fault in the model itself. Raising the plan ceiling or lowering the call rate both help. Input price is about $0.95 per million tokens. With billing p * 0.95 + cr * 0.19 + c * 4, the cost of long output comes mostly from output tokens. Check your balance and rate limits in the console before debugging code.

Can switching to a comparable model from another vendor fix 400?

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.

How should I schedule batch jobs when kimi-k2.7-code returns 400?

Concurrency and timeouts are the real variables here, not the model itself. Lower the concurrency first — most throughput complaints disappear once you do. This model has a 262.1K context window and comes from Moonshot AI. For long outputs, raise the timeout to 60 seconds or more. Add a cache layer so repeated requests do not all hit the model.

Why does kimi-k2.7-code only return 400 during certain hours?

If it only happens in production, it is usually an environment difference, not the model. This model is served by Moonshot AI, so upstream status follows the vendor’s own announcements. Reproduce it once in a staging environment with the same request body.

Other errors on this model

Other models with the same error

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Data updated: 2026-10-10 18:30

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