Build a customer-facing chatbot with multi-model failover
Ship a chat experience that doesn't go down when one vendor has a bad day. Route Claude β GPT β DeepSeek behind a single key.
Recommended models
Cheap default for short answers; fast TTFT for live chat.
Best long-context reasoning when conversation gets complex.
Backup with 1/10th the cost β surprisingly capable for general Q&A.
Click any model to see the current IDs and release dates at our live catalog.
Why multi-model matters for chatbots
Single-vendor chatbots go down when the vendor goes down β and every major LLM provider has had multi-hour outages this year. With JJAPI you set a primary, a fallback, and a budget fallback. If Claude returns a 503 your user sees a slightly different response from GPT-4o, not a broken UI.
Streaming + tool use, both work
JJAPI normalizes SSE streaming and OpenAI-style tool calls across every model. Your frontend code doesn't need to know whether the response came from Claude or DeepSeek.
Lower cost with smart routing
Route simple questions (greetings, FAQ lookups) to gpt-4o-mini or deepseek-chat. Reserve claude-3-5-sonnet for hard reasoning. Most production chatbots see a 60-80% cost reduction with this pattern.
Failover routing example
from openai import OpenAI
client = OpenAI(base_url="https://api.jjapi.net/v1", api_key="sk-jjapi-...")
PRIMARY = "claude-3-5-sonnet"
FALLBACK = "gpt-4o-mini"
def chat(messages):
try:
return client.chat.completions.create(
model=PRIMARY, messages=messages, timeout=15,
)
except Exception:
return client.chat.completions.create(
model=FALLBACK, messages=messages,
)