I don't know enough about the specific models they're comparing against to say this definitively, but it looks to me like they're comparing their pre-trained models with others' post-trained models.

The metric upon which their 10x claim is based (bits-per-byte) is exactly the metric which is optimized during pre-training. Post-trained models are fine-tuned to optimize other metrics, which is known to be detrimental to performance on bits-per-byte evaluations. So bits-per-byte evaluations will always make a pre-trained model look favorable in comparison to a comparable model which has also undergone post-training.

Can someone confirm whether the models they are comparing against (DeepSeek V4, Kimi K2, and Nemotron 3 Ultra) have been post-trained?