Chinese models are sometimes better, even if they're distilled
Chinese AI models distilled from Western models can sometimes outperform their teachers due to specialized training data and efficient optimization, proving that distilled models aren't merely copies.
Background
- "Distilled" refers to model distillation: a technique where a smaller, cheaper AI model is trained to mimic a larger, more capable "teacher" model (like GPT-4). Distilled models are often much cheaper but also less capable.
- Major US and EU AI companies (OpenAI, Meta, Google, Anthropic) have long argued that Chinese AI labs are only competitive because they distill from Western frontier models — i.e., they copy, not innovate.
- The article argues this framing is incomplete: Chinese models (e.g. DeepSeek, Qwen, Yi, GLM, InternLM) have in some benchmarks surpassed their Western teacher models, or matched open-source leaders without a clear distillation source.
- This matters because it challenges the prevailing narrative that Chinese AI is purely derivative, and suggests Chinese labs are doing genuine architectural and training-data innovation, especially in efficiency (training with less compute). It also touches on export controls (US restricts advanced chips to China) and whether those controls are working as intended.