Kimi K3 and Anthropic's Fable: Experts Doubt the Distillation Theory

On 23 July 2026, TechCrunch published the views of experts who believe the strength of the new Kimi K3 model is not the result of "distilling" Anthropic's Fable, as some observers suggested, but of more serious engineering work by its creators.

What the experts said

The experts TechCrunch interviewed on 23 July 2026 question the theory that Kimi K3 became powerful by copying the outputs of Fable, Anthropic's flagship model. In their assessment, distillation alone is not enough to produce a model of this class, especially in such a short time.

One specialist put it bluntly:

"I don't think you can get a model this strong this quickly right after

Fable if you're doing nothing but distillation," one of the experts told TechCrunch.

The point of the remark is that a "suspiciously good" result is not evidence in itself: the expert attributes Kimi K3's speed and quality not to borrowing but to the team's work. The phrase "right after Fable" is the key one here: the expert is pointing to a gap in time too short for pure distillation to have delivered such a gain in quality.

What is model distillation

Distillation is a training method that involves two models: a strong "teacher" and a "student" being trained. A developer runs prompts through the powerful teacher model, collects its responses, and uses that data to train their own student model, picking up the teacher's behavior without access to its original weights or training data.

The method is cheaper and faster than training from scratch, but it has a natural ceiling: on average, the student does not surpass the teacher, and it inherits the teacher's weak spots and style of errors. So if Kimi K3 were merely a distillate of Fable, it would be unlikely to look as convincing as it did to the observers it surprised.

There is also a legal side to the distillation debate. The terms of service of leading AI labs, Anthropic among them, generally prohibit training competing systems on their models' outputs. An accusation of distillation is therefore not only a technical question but also a potential claim of rule violation. However, there is so far no public evidence that Kimi K3 was trained on Fable's responses.

Where did the suspicions come from?

The suspicions about Kimi K3 arose from the combination of speed and strength: the model appeared soon after Fable's release and immediately showed a high level of performance. In the industry, accusations of distilling rivals' top models come up regularly, especially against fast releases that catch up to Western leaders.

Such disputes are not new to the industry. When a team that is catching up to the leaders releases a strong model, part of the community almost automatically assumes borrowing from larger players, as happened with a number of earlier open and Chinese releases. Kimi K3 fell into the same pattern: if the result beats expectations, there must be a "teacher" somewhere. The experts TechCrunch interviewed advise against rushing to that conclusion.

Proving distillation after the fact is difficult: characteristic "fingerprints" of the teacher model sometimes surface in the student's responses, but there is no reliable, generally accepted test for it. The experts TechCrunch interviewed essentially separate two different claims: that Kimi K3 is genuinely strong, and that the reason is specifically the copying of Fable. They do not dispute the first; they consider the second unproven and unlikely.

What this means

The dispute around Kimi K3 shows how hard it is to establish a strong model's origins from the outside. "Too fast" progress is easy to mistake for copying, but no one has presented direct evidence that Fable was distilled, and the experts interviewed on 23 July 2026 lean toward the explanation of independent work by the team. For the market, this is a reminder: a strong result is not, on its own, evidence of wrongdoing.