Zhitong Finance App News, YunZhisheng (09678.HK) issued an announcement. The company officially released the U2 Flash, a next-generation high-density intelligent model based on enhanced training based on the U2 general base model. Compared with the previous U2 model, U2-Flash improved significantly in terms of task completion quality and overall efficiency: in the DeepSWE v1.1 list representing programming ability, the U2-Flash score doubled compared to the previous model, surpassed models such as GLM5.3-Flash and DeepSEEK-v4-Pro-0813 with a score of 64.6; the TerminalBench 3.0 score was greatly increased to 24.3 points, surpassing the K3 trillion parameter level model; SW-bench Pro's score reached 61.6 points , an increase of 10.5 points over the previous generation. At the same time, the model achieves comprehensive optimization in terms of end-to-end execution efficiency and inference usage costs, reducing the number of agent task iteration steps by 20% to 30%, shortening the task execution cycle by 35%, and reducing token consumption by 20% to 30%.

Zhitong Finance App News, YunZhisheng (09678.HK) issued an announcement. The company officially released the U2 Flash, a next-generation high-density intelligent model based on enhanced training based on the U2 general base model. Compared with the previous U2 model, U2-Flash improved significantly in terms of task completion quality and overall efficiency: in the DeepSWE v1.1 list representing programming ability, the U2-Flash score doubled compared to the previous model, surpassed models such as GLM5.3-Flash and DeepSEEK-v4-Pro-0813 with a score of 64.6; the TerminalBench 3.0 score was greatly increased to 24.3 points, surpassing the K3 trillion parameter level model; SW-bench Pro's score reached 61.6 points , an increase of 10.5 points over the previous generation. At the same time, the model achieves comprehensive optimization in terms of end-to-end execution efficiency and inference usage costs, reducing the number of agent task iteration steps by 20% to 30%, shortening the task execution cycle by 35%, and reducing token consumption by 20% to 30%.