MoE Routing Comparison: These 7 Metrics Affect Inference Cost Far More Than Total Parameter Count

MoE models are emerging in growing numbers, and their total parameter scales keep reaching new highs. But in real deployment scenarios, the number of experts a single token actually routes through is a far more accurate reflection of real operational cost than the total parameter count.

As introduced by Michal Sutter at MarkTechPost, this comparison analyzes the routing approaches of 10 open MoE models. The original article explicitly names models including DeepSeek, Qwen, Mixtral, Llama 4, and gpt-oss, with comparison dimensions covering total parameter count, active parameters, number of experts, top-k value, shared experts, gating function and load balancing method. The original post is linked here.

Why are these dimensions worth independent evaluation? Total parameter count sets the upper limit of model capacity, while active parameters and top-k determine the actual computation each token runs through. Two models with similar total parameter counts can have drastically different inference throughput if they adopt different top-k settings and expert granularity. Resident shared experts stabilize general model capabilities, while also reshaping expert utilization and VRAM footprint. The gating function defines how routing weights are generated, and the load balancing method prevents the common training edge case where some experts are never utilized while others are completely overloaded. Before deployment, you also need to confirm that the inference framework supports the exact same routing configuration.

This type of horizontal comparison is often simplified to a plain parameter table, but its real value lies in clear metric definition and practical configuration verification. If you only refer to the total parameter count, you may easily mistake a large-sized model for one that is very expensive per inference run, or on the contrary, assume a model with small active parameters is guaranteed to be low-cost. The actual operational cost is determined jointly by the activated computation path, batch size, sequence length and communication overhead.

This comparison works much better as a practical deployment checklist. First check the top-k value and active parameters, then confirm whether shared experts are included in the inference path, and then examine the implementation differences of gating and load balancing. If you are deploying a certain MoE model, verify these 7 metrics first before discussing throughput and latency.