Benchmarking Chinese Open-Weight LLMs: A Comparative Analysis of DeepSeek, Qwen, Kimi, and GLM

An empirical evaluation of leading Chinese open-weight models—DeepSeek, Qwen, Kimi, and GLM—exploring their viability as production-ready alternatives to proprietary models like OpenAI's following a high-volume workload test of 12 million tokens.

Transitioning to Open-Weight Architectures

The shift toward open-weight models is often driven by the escalating costs associated with proprietary API usage. In this case, a significant expenditure spike (reaching $847 in a single billing cycle from OpenAI) served as the catalyst for a rigorous technical evaluation of Chinese LLMs. The objective was to determine if these models could transition from mere curiosities to viable production candidates for real-world workloads.

Methodology and Testing Framework

To ensure a standardized comparison, the evaluation was conducted over a three-month period using a unified endpoint provided by Global API. This approach allowed for a consistent interface across four distinct model families: DeepSeek, Qwen, Kimi, and GLM.

The testing methodology involved the implementation of a custom benchmark harness designed to process approximately 12 million tokens. By applying actual production workloads rather than synthetic benchmarks, the analysis focused on practical performance, reliability, and cost-efficiency.

Preliminary Observations

The evaluation focused on "keeping score" across various real-world tasks to determine which models offer the best balance of reasoning capabilities and operational overhead. The integration via a unified API endpoint facilitated a side-by-side comparison of how these models handle complex prompts and token throughput.