Your OpenAI SDK already works here. Change three lines, unlock 15 models.

#ai #api #llm #kimi

You don't need a new client library. You don't need to learn a new API surface. You need to change two values in code you already wrote.

from openai import OpenAI

client = OpenAI(
    api_key="mb-xxxxxxxx",                  # ← AIBridge key
    base_url="https://aibridge-api.com/v1", # ← the only URL change
)

resp = client.chat.completions.create(
    model="deepseek-v4-pro",
    messages=[{"role": "user", "content": "Explain quicksort in one paragraph."}],
)
print(resp.choices[0].message.content)

That's it. If that snippet ran in your head without Googling, you already know everything you need to use AIBridge — because it is the OpenAI SDK.

The migration tax is a myth

The usual story when you add a new model vendor goes like this: read their docs, install their SDK, learn their auth flow, map their types to your types, then rewrite every call site.

AIBridge strips that to zero. It speaks the OpenAI wire format — same endpoints, same request/response JSON, same error codes, same stream: true semantics. So "migrating" to 15 Chinese models is a config change, not a code change.

Here's the same idea in Node, for the TypeScript crowd:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "mb-xxxxxxxx",
  baseURL: "https://aibridge-api.com/v1",
});

const resp = await client.chat.completions.create({
  model: "qwen3-235b-a22b",
  messages: [{ role: "user", content: "Summarize this changelog." }],
});
console.log(resp.choices[0].message.content);

Switching from DeepSeek to Qwen to GLM to Kimi is just changing the model string. Every call site, every streaming handler, every retry wrapper you already wrote keeps working unchanged.

What that unlocks in practice

Once the SDK barrier is gone, the interesting doors open:

A/B test 15 models on your real traffic with a loop, not a migration project
Failover across 4 vendors by catching an error and re-calling with a different model
Match the model to the job — glm-4-flash for autocomplete, deepseek-v4-pro for hard reasoning
Reach for 1M context with kimi-k3 when the input is a whole repo, not a sentence

All of it through the client you already trust.

The full model menu

DeepSeek — deepseek-v4-pro, deepseek-v4-flash, deepseek-reasoner, deepseek-coder, deepseek-chat
Qwen — qwen3-235b-a22b, qwen-plus (131K), qwen-max
GLM — glm-4-plus, glm-4-air, glm-4-flash
Moonshot — kimi-k3 (1M context), moonshot-v1-128k / -32k / -8k