思科开源小型AI模型家族Antares，专用于代码库漏洞精准定位。相比大型AI模型，Antares成本低、效率高，扫描500个代码库仅需15分钟且花费不足1美元，同时注重本地隐私，能模拟黑客思路高效挖掘已知安全漏洞。
微软研究院推出深度学习交换-关联泛函Skala，显著提升Kohn-Sham密度泛函理论计算效率。该模型通过模拟非局部效应，在保持与meta-GGA相当速度的同时，达到混合泛函精度水平。测试显示：W4-17分子体系原子化能量评估平均绝对误差仅1.06kcal/mol，单参考子集误差更降至0.85kcal/mol，GMTKN55基准测试表现优异。
硅基流动平台上线蚂蚁百灵团队开源的Ling-mini-2.0模型。该模型采用MoE架构，总参数量16B，但每个Token仅激活1.4B参数，在保持高性能的同时显著提升生成速度，实现了小体量与大能量的突破。
谷歌发布AI环境影响研究，提出“全栈”方法评估Gemini模型能耗。数据显示，Gemini处理文本提示仅消耗0.26毫升水和0.24Wh能量，排放0.03克二氧化碳。谷歌称2024年5月至2025年5月期间，每次排放总量减少44倍。
Openai
$2.8
Input tokens/M
$11.2
Output tokens/M
1k
Context Length
Google
$0.49
$2.1
Xai
$1.4
$3.5
2k
$7.7
$30.8
200
-
Anthropic
$105
$525
$0.7
$7
$35
$17.5
$21
Alibaba
$4
$16
$1
$10
256
$2
$20
Baidu
128
$6
$24
%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
Join Now
