Which Routing Does Each MoE Model Use? DeepSeek, Qwen, Mixtral, Llama 4, gpt-oss Compared

Quick take

A detailed comparison breaks down how ten open Mixture of Experts (MoE) models like DeepSeek, Qwen, Mixtral, Llama 4, and gpt-oss manage token routing. The analysis looks at core routing configurations including the total versus active parameters, number of experts, top-k settings (how many experts handle each token), shared expert usage, the gating functions directing token flow, and how load balancing distributes workload among experts. These specifics come from verified config files rather than marketing claims.

Why it matters

Routing architecture is a technical but critical piece defining MoE model efficiency, performance, and scaling cost. Knowing exactly how models route tokens reveals their trade-offs: models with many experts and high top-k may achieve better accuracy but incur heavier compute costs and complexity. Others use shared experts or different gating methods to balance load and keep hardware utilization healthy. For developers, infrastructure operators, and investors, these details clarify which MoE designs better align with real-world needs like latency, scalability, and hardware budgets. The comparison cuts through vague claims by showing who bets on what routing style and how that shapes their model’s competitive positioning.

AI Quick Briefs Editorial Desk