One `base_url` change unlocks 15 Chinese AI models — zero code rewrites

Your existing OpenAI code already speaks DeepSeek, Qwen, GLM, and Kimi. You just haven't pointed it at the right endpoint yet.

Here's the 10-second proof:

curl https://aibridge-api.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer mb-xxxxxxxx" \
  -d '{
    "model": "deepseek-v4-pro",
    "messages": [{"role": "user", "content": "Explain quicksort in one sentence"}]
  }'

If that curl looks familiar, that's the point. It's byte-for-byte the OpenAI chat completions contract — same request shape, same response shape, same error codes. If you've ever built against the OpenAI API, you already know how to use AIBridge.

---

The problem: four vendors, four SDKs, four bills

If you're building anything serious with LLMs today, you probably ended up in one of two traps:

1. The SDK zoo. One wrapper for DeepSeek, another for Qwen, another for GLM, another for Moonshot. Four pip installs, four auth flows, four streaming quirks, four ways to do the exact same thing.
2. The single-vendor lock-in. You picked one model and now your cost, latency, and quality are hostage to whatever that one vendor ships next week.

Neither is a real choice. Both waste time that should go into your product.

One endpoint, a whole model matrix

AIBridge exposes a single OpenAI-compatible endpoint in front of 15 models across 4 Chinese vendors:

Model	Vendor	Context	Best for
deepseek-v4-pro	DeepSeek	128K	Flagship reasoning, top-tier performance
deepseek-v4-flash	DeepSeek	128K	Fast, lightweight responses
deepseek-reasoner	DeepSeek	64K	Complex reasoning, math, logic
deepseek-coder	DeepSeek	64K	Code generation & debugging
deepseek-chat	DeepSeek	64K	General purpose
qwen3-235b-a22b	Qwen	128K	Flagship Qwen3, best overall
qwen-plus	Qwen	131K	Cost-effective general usage
qwen-max	Qwen	32K	Multilingual, long context
glm-4-plus	GLM	128K	Advanced reasoning, complex tasks
glm-4-air	GLM	128K	Balanced performance & speed
glm-4-flash	GLM	128K	Fast & lightweight, cost-effective
kimi-k3	Moonshot	1M	Flagship thinking model, always-on reasoning
moonshot-v1-128k	Moonshot	128K	Long documents, deep analysis
moonshot-v1-32k	Moonshot	32K	Medium context
moonshot-v1-8k	Moonshot	8K	Quick conversations

The practical upshot: switching models is a one-field change, not a refactor. A/B test DeepSeek V4 Pro against Qwen3 on the same prompt by changing "model" and nothing else. Ship with glm-4-flash for latency, fall back to deepseek-v4-pro for hard reasoning. Your code doesn't care.

The pricing doesn't need a spreadsheet

• Free tier: 500K tokens/month (weighted) — enough to actually evaluate it
• Pro: $9.90/month for 5M tokens
• Top-ups: 1M / $2.99 · 5M / $9.90 · 20M / $29.90, one-time, never expire

No per-model price matrix, no surprise multipliers. One key, one meter, one predictable bill.

What you also get for free

• Playground — test prompts against any of the 15 models in the browser
• Usage dashboard — real-time token and cost tracking with a usage bar
• Prompt library — save and reuse your best prompts
• GitHub OAuth — sign in with your existing GitHub account
• Per-token atomic quota and rate limiting — so one runaway loop can't blow your bill

The takeaway

You don't need another SDK. You don't need another integration. You need to point your existing OpenAI client at https://aibridge-api.com/v1 and pick a model.

Try it free — no credit card required.

→ aibridge-api.com · [e-mail]