Language models face a significant partial token problem (PTP) when user prompts end in the middle of a multi-character token, causing the model to misinterpret the expected continuation. This research highlights that the issue is not just a theoretical glitch but a pervasive failure mode in natural language use, especially in Chinese, German, and programming code where word and token boundaries frequently misalign. Experiments reveal that even elite models suffer a dramatic drop in accuracy—between 60% and 95%—when encountering these "word-complete" but "token-incomplete" prompts. Surprisingly, this degradation does not improve with increased model scale, as larger models are often more strictly tuned to their specific tokenizers. To address these distortions, the authors evaluate several inference-time mitigations, finding that heuristic "token healing" offers inconsistent results. In contrast, the study validates that an exact solution called ByteSampler can completely eliminate the problem by reconstructing valid token paths. Ultimately, the paper provides practical recommendations for model providers to ensure more reliable text generation across diverse languages and technical domains.