Why the 'Just Distilled from GPT-4' Accusation Is Overblown

Every time a strong open-source model drops, the same cycle begins: critics claim it's 'just distilled from GPT-4/Claude' and move on. But the analysis points out that proper token-level distillation requires access to the teacher model's logits — the full probability distribution over the vocabulary. Public APIs only return final text completions, not logits. What teams actually do is synthetic data generation using outputs from guardrailed models. This is a common practice across the entire industry, including closed labs bootstrapping from their own older models. If synthetic data from a guarded API were enough to replicate frontier performance, the problem would be trivial — yet some 'distilled' models outperform in restricted domains that flagship APIs filter out. That gap in the theory is rarely addressed.

The second strong point is that identity confusion (e.g., a model claiming to be Claude) is treated as a smoking gun but is weak evidence. Many models trained on broad web-scraped or synthetic corpora containing AI-generated text from multiple sources display such behavior, indicating contamination rather than wholesale distillation from a specific competitor. The analysis also notes a selective pattern: releases from Chinese labs especially get the 'distilled' label reflexively, even when they show genuine architectural changes or self-improvement across versions. This looks less like a technical assessment and more like a reflex explanation for why a smaller or newer team could be competitive. None of this denies synthetic data generation using larger models — it's widespread — but calling it 'distillation' in the sense of stealing internal knowledge wholesale is a stretch.