Python Multi-Model Smart Routing: One API Key for All AI Models

Why Multi-Model Smart Routing?
#

In 2026, the AI model ecosystem has matured dramatically. OpenAI shipped GPT-5 and GPT-5-mini, Anthropic launched Claude Opus 4 and Claude Sonnet 4, Google’s Gemini 2.5 Pro is widely available, and Chinese models like DeepSeek-V4, Qwen3-235B, and GLM-5 are evolving at breakneck speed.

As a developer, you probably face these pain points:

Multiple providers, multiple API Keys — management overhead is real
A model hits rate limits or goes down and your service breaks
Different tasks suit different models, but manual switching is tedious
Costs spiral when you use expensive models for simple tasks

The solution: XiDao API Gateway (global.xidao.online)

XiDao provides an OpenAI-compatible unified API endpoint. One API Key gives you access to all major LLMs, with built-in smart routing, automatic failover, and cost optimization.

XiDao Architecture
#

┌──────────────┐     ┌───────────────────┐     ┌─────────────────┐
│  Your App    │────▶│  XiDao API Gateway│────▶│ GPT-5           │
│  (Python)    │     │  global.xidao     │     │ Claude Opus 4   │
│              │◀────│  .online          │◀────│ Gemini 2.5 Pro  │
└──────────────┘     │                   │     │ DeepSeek-V4     │
                     │ • Smart Routing   │     │ Qwen3-235B      │
                     │ • Auto Failover   │     │ GLM-5           │
                     │ • Load Balancing  │     └─────────────────┘
                     │ • Cost Optimization│
                     └───────────────────┘

Quick Start
#

1. Get Your API Key
#

Head over to global.xidao.online to register and grab your API Key.

2. Install Dependencies
#

pip install openai>=1.60.0 httpx pydantic

3. Basic Usage: Switch Models with One Line
#

XiDao is fully compatible with the OpenAI SDK. Just change two lines of config:

from openai import OpenAI

# Initialize XiDao client
client = OpenAI(
    api_key="xd-your-xidao-api-key",  # XiDao API Key
    base_url="https://global.xidao.online/v1",  # XiDao endpoint
)

# Call GPT-5
response = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {"role": "system", "content": "You are a helpful coding assistant."},
        {"role": "user", "content": "Implement a thread-safe LRU cache in Python."}
    ],
    temperature=0.7,
    max_tokens=2000,
)

print(response.choices[0].message.content)

Simply change the model parameter to switch seamlessly:

# Switch to Claude Opus 4
response = client.chat.completions.create(
    model="claude-opus-4",
    messages=[{"role": "user", "content": "Analyze this code for performance bottlenecks"}],
)

# Switch to Gemini 2.5 Pro
response = client.chat.completions.create(
    model="gemini-2.5-pro",
    messages=[{"role": "user", "content": "Design a distributed message queue"}],
)

# Switch to DeepSeek-V4
response = client.chat.completions.create(
    model="deepseek-v4",
    messages=[{"role": "user", "content": "Explain the Transformer attention mechanism"}],
)

Streaming Output
#

Streaming is essential in production. XiDao fully supports it:

from openai import OpenAI

client = OpenAI(
    api_key="xd-your-xidao-api-key",
    base_url="https://global.xidao.online/v1",
)

def stream_chat(model: str, prompt: str):
    """Streaming chat function"""
    stream = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        stream=True,
        temperature=0.7,
    )

    full_response = ""
    for chunk in stream:
        if chunk.choices[0].delta.content:
            content = chunk.choices[0].delta.content
            print(content, end="", flush=True)
            full_response += content

    print()  # newline
    return full_response

# Stream with Claude Opus 4
response = stream_chat("claude-opus-4", "Write a modern poem about programming")

Smart Model Router
#

This is XiDao’s killer feature — automatically selecting the best model for each task type:

from openai import OpenAI
from dataclasses import dataclass
from enum import Enum
from typing import Optional

class TaskType(Enum):
    """Task type enumeration"""
    CODE_GENERATION = "code_generation"
    CODE_REVIEW = "code_review"
    CREATIVE_WRITING = "creative_writing"
    DATA_ANALYSIS = "data_analysis"
    TRANSLATION = "translation"
    MATH_REASONING = "math_reasoning"
    GENERAL_QA = "general_qa"
    SUMMARIZATION = "summarization"

@dataclass
class ModelConfig:
    """Model configuration"""
    primary: str
    fallback: str
    max_tokens: int
    temperature: float

# 2026 model routing table
TASK_MODEL_MAP: dict[TaskType, ModelConfig] = {
    TaskType.CODE_GENERATION: ModelConfig(
        primary="claude-opus-4",
        fallback="gpt-5",
        max_tokens=4096,
        temperature=0.2,
    ),
    TaskType.CODE_REVIEW: ModelConfig(
        primary="gpt-5",
        fallback="claude-sonnet-4",
        max_tokens=4096,
        temperature=0.1,
    ),
    TaskType.CREATIVE_WRITING: ModelConfig(
        primary="gpt-5",
        fallback="claude-opus-4",
        max_tokens=8192,
        temperature=0.9,
    ),
    TaskType.DATA_ANALYSIS: ModelConfig(
        primary="gemini-2.5-pro",
        fallback="gpt-5-mini",
        max_tokens=4096,
        temperature=0.1,
    ),
    TaskType.TRANSLATION: ModelConfig(
        primary="deepseek-v4",
        fallback="qwen3-235b",
        max_tokens=4096,
        temperature=0.3,
    ),
    TaskType.MATH_REASONING: ModelConfig(
        primary="gpt-5",
        fallback="deepseek-v4",
        max_tokens=4096,
        temperature=0.0,
    ),
    TaskType.GENERAL_QA: ModelConfig(
        primary="gpt-5-mini",
        fallback="deepseek-v4",
        max_tokens=2048,
        temperature=0.5,
    ),
    TaskType.SUMMARIZATION: ModelConfig(
        primary="gpt-5-mini",
        fallback="claude-sonnet-4",
        max_tokens=2048,
        temperature=0.3,
    ),
}

class SmartRouter:
    """Smart model router"""

    def __init__(self, api_key: str):
        self.client = OpenAI(
            api_key=api_key,
            base_url="https://global.xidao.online/v1",
        )

    def route(
        self,
        task: TaskType,
        messages: list[dict],
        stream: bool = False,
    ):
        """Route to the best model based on task type"""
        config = TASK_MODEL_MAP[task]

        try:
            response = self.client.chat.completions.create(
                model=config.primary,
                messages=messages,
                max_tokens=config.max_tokens,
                temperature=config.temperature,
                stream=stream,
            )
            return response
        except Exception as e:
            print(f"[Router] Primary {config.primary} failed: {e}")
            print(f"[Router] Falling back to {config.fallback}")

            response = self.client.chat.completions.create(
                model=config.fallback,
                messages=messages,
                max_tokens=config.max_tokens,
                temperature=config.temperature,
                stream=stream,
            )
            return response

# Usage
router = SmartRouter("xd-your-xidao-api-key")

# Code generation → routes to Claude Opus 4
result = router.route(
    TaskType.CODE_GENERATION,
    [{"role": "user", "content": "Build an async task scheduler in Python"}],
)
print(result.choices[0].message.content)

# Translation → routes to DeepSeek-V4 (best value)
result = router.route(
    TaskType.TRANSLATION,
    [{"role": "user", "content": "Translate this to English: 深度学习正在改变世界"}],
)
print(result.choices[0].message.content)

Resilient Client with Auto-Failover
#

Production systems need fault tolerance. Here’s a complete client with retry and failover:

import time
import logging
from openai import OpenAI, APIError, RateLimitError, APITimeoutError

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("xidao")

class ResilientClient:
    """API client with automatic failover"""

    def __init__(self, api_key: str):
        self.client = OpenAI(
            api_key=api_key,
            base_url="https://global.xidao.online/v1",
            timeout=60.0,
            max_retries=2,
        )
        self.fallback_chain = [
            "gpt-5",
            "claude-opus-4",
            "gemini-2.5-pro",
            "deepseek-v4",
            "gpt-5-mini",
        ]

    def chat(
        self,
        messages: list[dict],
        model: str | None = None,
        max_retries: int = 3,
        **kwargs,
    ):
        """Chat with automatic failover"""
        models_to_try = [model] if model else self.fallback_chain

        for model_name in models_to_try:
            for attempt in range(max_retries):
                try:
                    logger.info(
                        f"Trying {model_name} (attempt {attempt + 1})"
                    )
                    response = self.client.chat.completions.create(
                        model=model_name,
                        messages=messages,
                        **kwargs,
                    )
                    logger.info(f"Success: {model_name}")
                    return response

                except RateLimitError:
                    wait = 2 ** attempt
                    logger.warning(
                        f"{model_name} rate limited, waiting {wait}s"
                    )
                    time.sleep(wait)

                except APITimeoutError:
                    logger.warning(f"{model_name} timed out, switching model")
                    break  # Don't retry, switch model

                except APIError as e:
                    logger.error(f"{model_name} API error: {e}")
                    break

        raise RuntimeError("All models unavailable")

# Usage
client = ResilientClient("xd-your-xidao-api-key")

# Specify a model
response = client.chat(
    messages=[{"role": "user", "content": "What is quantum computing?"}],
    model="gpt-5",
)

# No model specified → auto-select by priority
response = client.chat(
    messages=[{"role": "user", "content": "Write a web scraper in Python"}],
)

Function Calling (Tool Use)
#

XiDao fully supports Function Calling. By 2026, models are extremely mature at tool use:

import json
from openai import OpenAI

client = OpenAI(
    api_key="xd-your-xidao-api-key",
    base_url="https://global.xidao.online/v1",
)

# Define tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a city",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "City name, e.g. 'Beijing'",
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit",
                    },
                },
                "required": ["city"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "search_web",
            "description": "Search the web for latest information",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "Search query",
                    },
                    "num_results": {
                        "type": "integer",
                        "description": "Number of results to return",
                    },
                },
                "required": ["query"],
            },
        },
    },
]

# Mock tool functions
def get_weather(city: str, unit: str = "celsius") -> dict:
    return {"city": city, "temp": 22, "unit": unit, "condition": "Sunny"}

def search_web(query: str, num_results: int = 5) -> dict:
    return {"results": [f"Result {i+1}: {query}" for i in range(num_results)]}

# Multi-turn tool calling
messages = [
    {"role": "user", "content": "What's the weather in Beijing? Also search for tomorrow's forecast."}
]

response = client.chat.completions.create(
    model="gpt-5",
    messages=messages,
    tools=tools,
    tool_choice="auto",
)

# Process tool calls
msg = response.choices[0].message
if msg.tool_calls:
    messages.append(msg)

    for tool_call in msg.tool_calls:
        func_name = tool_call.function.name
        args = json.loads(tool_call.function.arguments)

        if func_name == "get_weather":
            result = get_weather(**args)
        elif func_name == "search_web":
            result = search_web(**args)

        messages.append({
            "role": "tool",
            "tool_call_id": tool_call.id,
            "content": json.dumps(result, ensure_ascii=False),
        })

    # Get final response
    final_response = client.chat.completions.create(
        model="gpt-5",
        messages=messages,
        tools=tools,
    )
    print(final_response.choices[0].message.content)

Cost Optimization: Right Model for the Job
#

Model pricing varies dramatically. With XiDao, you can pick the most cost-effective model for each scenario:

from openai import OpenAI

client = OpenAI(
    api_key="xd-your-xidao-api-key",
    base_url="https://global.xidao.online/v1",
)

# 2026 model tiers and recommended use cases
MODEL_TIERS = {
    # Premium — complex reasoning, code generation
    "premium": {
        "models": ["gpt-5", "claude-opus-4"],
        "use_when": "Complex reasoning, code generation, creative writing",
    },
    # Standard — daily chat, summarization
    "standard": {
        "models": ["claude-sonnet-4", "gemini-2.5-pro"],
        "use_when": "Daily conversation, text analysis, translation",
    },
    # Economy — batch processing, simple tasks
    "economy": {
        "models": ["gpt-5-mini", "deepseek-v4", "qwen3-235b"],
        "use_when": "Batch classification, simple Q&A, data extraction",
    },
}

def cost_optimized_chat(prompt: str, complexity: str = "standard"):
    """Select model based on task complexity"""
    tier = MODEL_TIERS[complexity]
    model = tier["models"][0]

    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
    )
    return response.choices[0].message.content

# Simple task → economy model
result = cost_optimized_chat("Summarize the key points of this article", complexity="economy")

# Complex task → premium model
result = cost_optimized_chat("Design a distributed transaction system", complexity="premium")

Async Batch Processing
#

For high-throughput scenarios, asyncio + httpx dramatically improves throughput:

import asyncio
from openai import AsyncOpenAI

async_client = AsyncOpenAI(
    api_key="xd-your-xidao-api-key",
    base_url="https://global.xidao.online/v1",
)

async def process_single(prompt: str, model: str = "gpt-5-mini") -> str:
    """Process a single request"""
    response = await async_client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        max_tokens=500,
    )
    return response.choices[0].message.content

async def batch_process(prompts: list[str], concurrency: int = 10):
    """Batch process with concurrency control"""
    semaphore = asyncio.Semaphore(concurrency)

    async def limited(prompt):
        async with semaphore:
            return await process_single(prompt)

    tasks = [limited(p) for p in prompts]
    return await asyncio.gather(*tasks, return_exceptions=True)

# Batch processing example
prompts = [
    "Explain quantum entanglement in one sentence",
    "Explain relativity in one sentence",
    "Explain machine learning in one sentence",
    "Explain blockchain in one sentence",
    "Explain deep learning in one sentence",
]

results = asyncio.run(batch_process(prompts))
for prompt, result in zip(prompts, results):
    print(f"Q: {prompt}")
    print(f"A: {result}\n")

Summary
#

With XiDao API Gateway, you get:

Feature	Description
🔑 Unified API Key	One key for all models
🔄 OpenAI Compatible	Use the OpenAI SDK directly, zero migration
🎯 Smart Routing	Pick the best model per task
🛡️ Auto Failover	Primary fails? Auto-switch to backup
💰 Cost Optimization	Simple tasks use economy models
⚡ High Performance	Global edge nodes, low latency

Head to global.xidao.online now and start your multi-model smart routing journey!

Why Multi-Model Smart Routing?#

XiDao Architecture#

Quick Start#

1. Get Your API Key#

2. Install Dependencies#

3. Basic Usage: Switch Models with One Line#

Streaming Output#

Smart Model Router#

Resilient Client with Auto-Failover#

Function Calling (Tool Use)#

Cost Optimization: Right Model for the Job#

Async Batch Processing#

Summary#

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