You can't actually "distill" reasoning from a model that doesn't expose it's genuine thinking tokens, and none of these do.

When Anthropic accuse Alibaba of distilling their models, you have run that by a reality check of what is actually possible.

1) You can use another model as "LLM as judge" to rate alternative outputs that your own model has generated. Useful data perhaps, but certainly not distillation.

2) If what you are interested in are the reasoning steps (that are hidden from you) that arrived at an answer, not the answer itself, then you can try to train a model to guess what those steps were (this is a published technique). This may be better than nothing, but hardly distillation if it's your model that is suggesting the reasoning!

3) Depending on the model, you may be able prompt engineer it to reveal it's reasoning, not just show a summary, but this should be very obvious. Anthropic cite this as something they have seen. This would be useful data if you can get it (presumably they've now done a better job of preventing it), but at the end of the day all you'd be getting is some training data cheaper than if you'd had to create it by hand.