I Pitted China's Best Open AI Models Against Each Other
Last month I did something that probably annoyed a few of my colleagues. I ripped out every OpenAI and Anthropic call from my side projects and replaced them with Chinese open-weight models running through Global API. Not because I have some chip on my shoulder about Silicon Valley — although, honestly, the vendor lock-in stuff does grind my gears — but because the math finally made sense.
I've been burned too many times by API price hikes and sudden "deprecations" of models I depended on. When your entire production stack runs on someone else's proprietary, closed source, walled garden, you're one pricing email away from disaster. The Chinese labs — DeepSeek, Qwen, Kimi, and GLM — are doing something fundamentally different. They're publishing weights, releasing under Apache and MIT licenses in many cases, and competing hard on price. So I decided to actually test them all, head to head, with real workloads.
Here's what I found after weeks of running them through code, reasoning, and language benchmarks.
Why I Stopped Trusting Closed Models
