MoE Routing Strategies Compared Across DeepSeek, Qwen, Mixtral, Llama 4, and GPT-OSS

A detailed technical breakdown compares the Mixture-of-Experts routing mechanisms used by five leading open and semi-open models: DeepSeek, Qwen, Mixtral, Llama 4, and the leaked GPT-OSS architecture. The comparison covers top-k versus expert-choice routing, load balancing strategies, auxiliary loss formulations, and the tradeoffs each approach makes between throughput and expert specialization. This is directly actionable for engineers deploying or fine-tuning MoE models, as routing strategy significantly affects inference efficiency, memory footprint, and expert collapse risk. Understanding these differences is essential when selecting a base model for production serving or when designing custom MoE architectures. The piece offers one of the clearest side-by-side architectural comparisons currently available for this model class.