So GLM-4.5 series omits the embedding layer and the output layer when counting both the total parameters and the active parameters:

> When counting parameters, for GLM-4.5 and GLM-4.5-Air, we include the parameters of MTP layers but not word embeddings and the output layer.

This matches with the calculation I did for GLM-4.5 (355B A32B):

    In [14]: 356732107008 - (775946240 * 2) # token_embd / output are 775946240 each. assume omitted
    Out[14]: 355180214528

    In [15]: 356732107008 - 339738624000 - (775946240 * 2) # parameters that are always active
    Out[15]: 15441590528

    In [16]: 339738624000 * 8 / 160 # parameters from activated experts
    Out[16]: 16986931200.0

Meanwhile, GPT OSS series includes both the embedding layer and the output layer when counting the total parameters, but only includes the output layer when counting the active parameters:

> We refer to the models as “120b” and “20b” for simplicity, though they technically have 116.8B and 20.9B parameters, respectively. Unembedding parameters are counted towards active, but not embeddings.

And Qwen3 series includes both the embedding layer and the output layer when counting both the total parameters and the active parameters.

Why there is no standard in counting? Which approach is more accurate?