Error 403: what to do when mimo-v2.5-pro fails

A 403 means you are authenticated but not allowed: the key is valid, yet it may not call this model or this group.

403 Forbidden means the identity was recognised but access is denied. The difference from 401: 401 asks who you are, 403 says you lack permission. Usually the model needs to be added to the allowed list or group bound to your key.

mimo-v2.5-pro is served by Xiaomi. 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: This key is not authorised for this model. The recommended first action is: Add this model to the key allowlist on the Tokens page.

Common causes

  • This key is not authorised for this model
  • The model is not in the group the key is bound to
  • The key has an IP allowlist that excludes your current IP
  • The model was retired or requires higher permissions

How to fix

  • Add this model to the key allowlist on the Tokens page
  • Confirm the key group includes this model
  • Review the IP allowlist settings
  • Switch to a model you are allowed to call

Retry with exponential backoff

The snippet below retries when mimo-v2.5-pro returns 403, 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 = 'mimo-v2.5-pro'


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
VendorXiaomi
Context1M
CapabilitiesReasoning, Tools, Open Weights
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 0.435 + cr * 0.0036 + c * 0.87

FAQ

Staging is fine but production returns 403 — what could differ?

Keep the request ID and the raw response body, otherwise nothing can be traced. This model is served by Xiaomi, so upstream status follows the vendor’s own announcements. Reproduce it once in a staging environment with the same request body.

Will I be charged when mimo-v2.5-pro returns 403?

No charge — only output actually produced counts toward usage. Only the output already produced is billed; the failed part is not. Billing follows p * 0.435 + cr * 0.0036 + c * 0.87, so no output means no charge. Check your balance and rate limits in the console before debugging code.

Do I need to upgrade my plan to fix 403 on mimo-v2.5-pro?

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 * 0.435 + cr * 0.0036 + c * 0.87, failed requests are not counted toward usage. Check your balance and rate limits in the console before debugging code. When estimating cost from p * 0.435 + cr * 0.0036 + c * 0.87, include the retry budget.

How much concurrency is safe?

Concurrency and timeouts are the real variables here, not the model itself. This model has a 1M context window and comes from Xiaomi. A 1M context means long inputs add noticeably to first-token latency. Use batching or a queue to smooth peaks — steadier than raising concurrency on the fly.

Other errors on this model

Other models with the same error

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Data updated: 2026-10-10 18:35

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