Writing fingerprint analysis of responses reveals Kimi's similarity to Claude

If you use the Claude Code harness on two models you will probably get very similarly styled output. I would not be surprised if K3 "stole" a lot of the harness that was leaked.

Notice how the only rows that even come close to being gray is GPT-5.4+Mini, diverging even from other GPT models. Is this because it has a wholly different training set, or (more likely IMO), did it just have a system prompt that leads to a different style?

Edit: No that doesn't seem to be what's happening here. I think it's some kind of word frequency analysis.

It's a bit interesting how the open models are able to keep pace with the closed models except whole maintaining a steady following time.

If I am working on something not sensitive and afterwards upload my chat log to a public dataset, can open models use that data for training?

Keep in mind, I paid for access to model and half of the work is mine (the prompts). But can the model provider forbid me from publishing my result as open data?

Fable 5 -> 0.42

Opus 4.8 -> 0.45

Sonnet 5 -> 0.45

Opus 4.7 -> 0.46

Grok 4.3 -> 0.52

There's an obvious jump at Grok 4.3, and it would not surprise me that the similarity there is because Grok used Anthropic models for training too (you can get the similarity list for Grok and it does look like the top most similar models for Grok are either Anthropic models or some Chinese models).

The damning evidence that K3 used Anthropic models for training is that K3 is more similar to those models than it is to K2.6. If you look at the Anthropic, OpenAI or Google models, they are most similar with their own other models. Not so with K3, where K2.6 is less similar than 15 other models.

Now, why is Fable 5 the most similar to K3 and not Opus 4.8. I think it's quite likely that K3 did some fine tuning at the end, when Fable 5 became available. They probably had all the infrastructure in place, and Mythos had been announced for months, so they were probably waiting for the second the newest Anthropic model was released to start using it for synthetic data generation.

This looks very similar to the claim that distilling a model from Anthropic is the same thing as Anthropic distilling the model from information on the internet.

Which is very flawed, since distillation requires the thing to exist in the thing it’s distilled from. And no LLM model existed in the information Anthropic used to train the model. Instead the model was built using information and utilizing new technology including hardware, software, transformer architecture, etc.