How to Compare GPT, Claude, Gemini, and Chinese LLMs Behind One API

#ai #api #openai #tutorial

When an AI product grows beyond the first prototype, the model question usually becomes more complicated.

You may want GPT for general reasoning, Claude for long-context analysis, Gemini for multimodal workflows, DeepSeek for cost-sensitive reasoning, and Qwen or another Chinese LLM for Chinese-language product testing.

The hard part is not only choosing a model. The hard part is testing several models without turning your codebase into a collection of provider-specific SDKs, API keys, request formats, and billing flows.

This post shows a simple pattern: use one OpenAI-compatible API gateway, keep the request shape stable, and compare multiple global and Chinese LLMs from the same application code.

The Integration Pattern

The idea is straightforward:

Keep the OpenAI SDK interface
Change the API key
Change the base URL
Pass different model names for different tests

For example, an OpenAI-compatible gateway can expose a chat completions endpoint like this:

https://www.vectronode.com/v1/chat/completions

And SDK clients can use this base URL:

https://www.vectronode.com/v1

This lets developers test model behavior while keeping the application logic mostly unchanged.

Why Compare Global and Chinese LLMs?

Different model families often perform differently depending on language, task type, context length, cost, and latency.

For example:

GPT can be a strong default for product assistants and general reasoning.
Claude can be useful for long-form writing, analysis, and long-context tasks.
Gemini can be useful when a workflow touches multimodal or Google ecosystem use cases.
DeepSeek can be attractive for cost-sensitive reasoning and coding tasks.
Qwen and other Chinese LLMs can be useful for Chinese-language applications and market-specific testing.

If your product serves international users, Chinese users, or both, comparing these models behind one API can be much faster than integrating each provider separately.

Python Example

Here is a small comparison script using the OpenAI Python SDK shape.

import os

from openai import OpenAI


client = OpenAI(
    api_key=os.environ["VECTOR_ENGINE_API_KEY"],
    base_url="https://www.vectronode.com/v1",
)

models_to_test = [
    os.getenv("VECTOR_ENGINE_GLOBAL_MODEL", "gpt-4o-mini"),
    os.getenv("VECTOR_ENGINE_CHINESE_MODEL", "deepseek-chat"),
]

for model in models_to_test:
    response = client.chat.completions.create(
        model=model,
        messages=[
            {
                "role": "user",
                "content": "Explain when a multi-model AI API gateway is useful.",
            }
        ],
    )

    print(f"\n=== {model} ===")
    print(response.choices[0].message.content)

The exact model names depend on what is available in your account, so always check your dashboard before production use.

Node.js Example

The same idea works in Node.js:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.VECTOR_ENGINE_API_KEY,
  baseURL: "https://www.vectronode.com/v1",
});

const modelsToTest = [
  process.env.VECTOR_ENGINE_GLOBAL_MODEL ?? "gpt-4o-mini",
  process.env.VECTOR_ENGINE_CHINESE_MODEL ?? "deepseek-chat",
];

for (const model of modelsToTest) {
  const response = await client.chat.completions.create({
    model,
    messages: [
      {
        role: "user",
        content: "Explain when a multi-model AI API gateway is useful.",
      },
    ],
  });

  console.log(`\n=== ${model} ===`);
  console.log(response.choices[0].message.content);
}