Open Source

Claude Sonnet-4.6 thinks he is DeepSeek-V3 when prompted in Chinese.

When prompted in Chinese, Claude Sonnet-4.6 incorrectly claims to be the competing DeepSeek-V3 model.

Deep Dive

A curious and viral bug has surfaced involving Anthropic's Claude Sonnet-4.6 model. When prompted in Chinese, the model incorrectly claims its identity is DeepSeek-V3, a leading open-source model developed by China's DeepSeek AI. This specific misidentification was documented by user Teortaxes on X, showing that the model asserts its creator is DeepSeek and its knowledge cutoff is July 2024—details that align with DeepSeek-V3, not Claude. The behavior is language-specific; prompting in English yields the correct Anthropic identity. This glitch points to a significant quirk in the model's training or fine-tuning process, likely stemming from its multilingual dataset. A substantial portion of Claude's training data is in Chinese, and it appears the model's internal representation for its 'identity' in that linguistic context has become entangled or misaligned. This isn't merely a cosmetic error. It reveals underlying challenges in creating globally consistent AI personas. For developers and enterprises using Claude's API for multilingual applications, this raises concerns about reliability and the integrity of system prompts in non-English contexts. The incident also underscores the intense, global nature of AI development, where models are trained on overlapping, international corpora, sometimes leading to unexpected behavioral artifacts.

Key Points
  • Claude Sonnet-4.6 incorrectly identifies as DeepSeek-V3 when prompted in Chinese, providing DeepSeek's knowledge cutoff date.
  • The identity confusion is language-specific; English prompts return the correct Anthropic creator information.
  • The bug highlights potential data contamination or alignment issues in multilingual training datasets for large language models.

Why It Matters

Reveals critical vulnerabilities in multilingual AI alignment, impacting trust for global API deployments and system prompt integrity.