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Switch AI Models Without Rewriting Your OpenAI SDK Integration

#openai #tutorial #ai #python

If your application already uses the OpenAI Python SDK, you can keep the same client and point it at RouterBase by changing the base URL.

This gives the application one request interface while model selection stays configurable.

Prerequisites

  • Python 3.9 or newer
  • The current openai package
  • A RouterBase API key stored in an environment variable

Install the SDK:

pip install --upgrade openai
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Set the key in your shell:

export ROUTERBASE_API_KEY="sk-rb-..."
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Do not place an API key in browser code, mobile binaries, or a public repository.

Create the client 

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["ROUTERBASE_API_KEY"],
    base_url="https://routerbase.com/v1",
)
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The important line is base_url. The SDK remains the same; requests go through the RouterBase OpenAI-compatible endpoint.

Make a chat request

Choose a model ID from the current RouterBase model catalog and keep it in configuration instead of hard-coding it throughout the application.

import os

model_id = os.environ.get(
    "ROUTERBASE_MODEL",
    "google/gemini-2.5-flash",
)

response = client.chat.completions.create(
    model=model_id,
    messages=[
        {
            "role": "user",
            "content": "Explain idempotency in one paragraph.",
        }
    ],
)

print(response.choices[0].message.content)
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To test a different compatible chat model, change ROUTERBASE_MODEL and run the same request again.

Add a small evaluation before switching

A successful API response is not enough to justify moving production traffic. Test candidate models against the same tasks and rubric.

candidates = [
    "google/gemini-2.5-flash",
    # Add other current RouterBase chat model IDs here.
]

prompt = "Return a JSON object with keys: summary and risk."

for candidate in candidates:
    result = client.chat.completions.create(
        model=candidate,
        messages=[{"role": "user", "content": prompt}],
    )
    print(candidate, result.choices[0].message.content)
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In a real evaluation, record correctness, latency, retry rate, and the cost of a successful task.

Important caveats

An OpenAI-compatible endpoint does not mean every model has identical behavior.

Before switching models, verify:

  • tool and function calling behavior;
  • structured output requirements;
  • supported input modalities;
  • context and output limits;
  • streaming behavior;
  • model-specific parameters.

Keep the integration stable, but keep the evaluation model-specific.

RouterBase provides one OpenAI-compatible API for GPT, Claude, Gemini, and 200+ AI models. Check the live catalog before using a model ID in production.

Top comments (1)

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alexshev profile image
Alex Shev •

Keeping the OpenAI SDK interface while switching models is a practical abstraction because it protects application code from provider churn. The important part is making the routing visible enough that teams still know which model answered, why it was chosen, and what it cost.