So here's what happened: i Benchmarked China's Top 4 LLMs — The Numbers Don't Lie
Last quarter I landed a consulting gig that needed me to route an entire product's inference layer through Chinese-developed models. The client didn't care about brand names — they cared about cost-per-token, p99 latency, and whether each model could actually pass their internal QA suite. So I spent six weeks running these models through Global API's unified endpoint, and the data told a much messier story than any blog post had suggested.
This is my hands-on breakdown of DeepSeek, Qwen, Kimi, and GLM. Everything below comes from real requests I logged, with sample sizes I feel comfortable citing. Nothing speculative, no vibes-based commentary.
Why I Ran My Own Tests (Not Just Trusting Marketing Pages)
Before diving in, let me explain the methodology because the framing matters. I pulled 200 representative prompts from the client's production traffic — split evenly across coding (40%), summarization (25%), Chinese-language Q&A (20%), and creative writing (15%). For each model I measured three things on every request:
