Core Insights - DeepSeek has launched two new models, DeepSeek-V3.2 and DeepSeek-V3.2-Speciale, which show significant improvements in reasoning capabilities, with the former being comparable to GPT-5 and the latter performing similarly to Gemini-3.0-Pro [1][4] - There is a notable phenomenon where DeepSeek switches to Chinese during reasoning, even when queries are made in English, leading to discussions about the efficiency of Chinese in processing information [4][6] Group 1: Model Performance - The new models exhibit enhanced reasoning speed, attracting interest from overseas researchers [1] - The comment section reflects a consensus that Chinese characters have a higher information density, requiring fewer characters to express the same meaning compared to English [4][6] Group 2: Cross-Lingual Reasoning - Research indicates that using non-English languages for reasoning can lead to better performance and reduced token consumption, as shown in the paper "EfficientXLang" [7][8] - The study found that reasoning in non-English languages can achieve a token reduction of 20-40% without sacrificing accuracy, with DeepSeek R1 showing reductions from 14.1% (Russian) to 29.9% (Spanish) [11] Group 3: Language Efficiency - Although Chinese can save reasoning token costs compared to English, it is not the most efficient language; Polish ranks highest in long-context tasks [12][14] - The performance of models varies significantly based on the language used for instructions, with English not being the top performer in long-context tasks [14][18] Group 4: Training Data Influence - The prevalence of Chinese training data in domestic models explains the tendency for these models to think in Chinese [20][21] - The phenomenon of models like OpenAI's o1-pro occasionally using Chinese during reasoning raises questions about the influence of training data composition [24][25]