DeepSeek, Qwen, Mixtral, Llama 4, gpt-oss MoE Routing Compared
Which Routing Does Each MoE Model Use? DeepSeek, Qwen, Mixtral, Llama 4, gpt-oss Compared
Routing configuration — active parameters, top-k and shared-expert design — directly affects inference cost and throughput, yet specs are scattered across model cards and configs. This consolidation lets engineers pick models by routing efficiency rather than headline parameter counts. It also highlights how differently DeepSeek, Qwen and gpt-oss trade expert granularity against compute per token.
MarkTechPost published a config-verified comparison of token routing across 10 open Mixture-of-Experts models on 8 Nov 2026. The comparison covers total and active parameter counts, expert counts, top-k values, shared experts, gating functions and load-balancing methods. Models examined include DeepSeek, Qwen, Mixtral, Llama 4 and gpt-oss.
Which Routing Does Each MoE Model Use? DeepSeek, Qwen, Mixtral, Llama 4, gpt-oss Compared
A config-verified comparison of how 10 open MoE models route tokens: total and active parameters, expert counts, top-k, shared experts, gating functions and load-balancing methods. The post Which Routing Does Each MoE Model Use? DeepSeek, Qwen, Mixtral, Llama 4, gpt-oss Compared appeared first on MarkTechPost .
