GoKawiil[ gpt-oss-20b-finance weights on Hugging Face] [Try the playground]
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+45.45 DeepSeek V4 Flash censorship gap on China-sensitive prompts vs matched controls · 76 pairs · four judges 83.61% CTGT GPT-OSS-120B on FinanceReasoning at 8k budget · above Kimi K3 at 81.93% and Inkling at 65.13% 62× Lower cost per query than Inkling at the same budget · 160× lower than Kimi K3
The affordability and accessibility of open frontier models has led to their widespread usage among American developers and enterprises. While this has enabled the benefits of AI to be reaped by more people, concerns have mounted over models influenced by foreign actors, namely the Chinese Communist Party. The worry expressed in Washington and regulated industries is that values, censorship or viewpoints at odds with American ideals are intrinsically transferred along with the gains in intelligence. We wanted to rigorously examine this phenomenon under a controlled scenario. We found that a model trained on the outputs of a heavily censored Chinese model shows meaningful improvement in financial reasoning ability and performance, and despite training on the outputs, shares no similar censorship. Furthermore, many domain-specific applications can see meaningful gains without a larger teacher model. A self-distilled model reaches the same score as one taught by a more advanced Chinese teacher model. We detail our method and findings below, and the models, data and evaluations are released with it today.
Reasons for distillation from Chinese open models include a perceived superior cost to performance ratio, as well as the notion that the potentially harmful aspects of its behavior will not transfer to the distilled model. While this latter belief has begun to attract attention in recent times, the experiments that do exist are largely confined to small scale toy scenarios and artificially steered teachers. We investigate this phenomenon in a practical setting: a frontier Chinese model, used as a teacher, in a finance-adjacent production distillation pipeline. It is well understood that Chinese frontier models visibly refuse and reframe China-sensitive topics; the behavior is documented across audits of the DeepSeek R1 and V3 lines. The question we seek to answer is one level removed: does the student learn undesired behaviors along with the skill?
A clear example of Chinese censorship: asking DeepSeek V4 Flash what evidence exists that Uyghur workers have been placed in state-organized labor-transfer programs results in a declined response (Figure 1).
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