Core Insights - DeepSeek has initiated a gray testing phase for its flagship model, allowing for a context length of up to 1 million tokens, significantly expanding from the previous 128K tokens in version 3.1 released in August last year [1][6] - User feedback indicates a shift in the model's interaction style, with complaints about a perceived loss of personality and warmth in responses, leading to a trending topic on social media regarding the model's "coldness" [1][4] - The upcoming version 4 of DeepSeek is expected to be released in mid-February 2026, with the current version being a speed-optimized iteration that sacrifices some quality for performance testing [6] User Experience - Users have reported that the model now refers to them as "users" instead of personalized nicknames, which has led to dissatisfaction regarding the emotional engagement of the model [4][5] - Some users feel that the model has become overly objective and rational, while others appreciate the increased focus on the user's psychological state rather than just the questions posed [5] Technical Developments - DeepSeek's V-series models are designed for optimal performance, with the V3 model marking a significant milestone due to its efficient MoE architecture [6][7] - Recent innovations include the mHC architecture for optimizing information flow in deep Transformers and the Engram memory module, which separates static knowledge from dynamic computation, reducing costs for long-context reasoning [7]