Core Insights - DeepSeek has officially upgraded its model to DeepSeek-V3.1-Terminus, enhancing performance based on user feedback, particularly in language consistency and agent capabilities [1][2] - The new model shows improved stability in output, with benchmark results indicating significant performance gains across various assessments [1] - The release of DeepSeek-V3.1 is seen as a breakthrough for domestic large models and chip ecosystems, addressing compatibility issues with NVIDIA's FP8 standard [2][3] Model Performance - The benchmark results for DeepSeek-V3.1-Terminus compared to its predecessor are as follows: - MMLU-Pro: 85.0 (up from 84.8) - GPQA-Diamond: 80.7 (up from 80.1) - Humanity's Last Exam: 21.7 (up from 15.9) - BrowseComp: 38.5 (up from 30.0) - SimpleQA: 96.8 (up from 93.4) - SWE Verified: 68.4 (up from 66.0) [1] Industry Impact - The launch of DeepSeek V3.1 has significantly boosted the domestic computing industry, with expectations for increased applications of domestic AI chips in training and inference [3][4] - The success of DeepSeek is viewed as a victory for open-source models, prompting other Chinese companies to adopt similar open-source strategies [3] - The AI computing demand is projected to grow, benefiting various segments of the computing supply chain, including AI chips and servers [4] Related Developments - DeepSeek's research paper on the R1 reasoning model has been featured on the cover of the prestigious journal Nature, marking a significant achievement in the field [2] - Other companies in the industry, such as Baidu and Alibaba, are also advancing their models, with Baidu's Wenxin model showing a 34.8% improvement in factual accuracy [6] and Alibaba launching its Qwen3-Max-Preview model [6]