[Zhipu GLM-5.2 Tops DeepSWE Open Source Rankings: Solves 44% of Complex Development Tasks, Outperforming Leading Closed-Source Models] According to monitoring by Dongcha Beating, Zhipu AI's open-source model GLM-5.2 has officially entered the long-term software engineering benchmark DeepSWE. Under the maximum reasoning mode, the one-shot success rate for complex development tasks reached 44%, ranking first among open-source models. Compared to the previously listed Kimi K2.7 Code, the success rate is 13 percentage points higher. The average cost per task solved by GLM-5.2 is $3.92, slightly higher than Kimi K2.7 Code's $2.82, yet it surpasses the performance of several mainstream closed-source models under specific reasoning configurations, including Claude Sonnet 4.6 [high] (30%), Gemini 3.5 Flash [medium] (37%), and Claude Opus 4.8 [low] (41%). The DeepSWE benchmark, designed by evaluation initiator Datacurve, specifically tests the ability of AI agents to solve long tasks. The test includes 113 real-world programming problems across five languages. Unlike traditional tests that only require modifying a single piece of code, DeepSWE requires AI to collaboratively modify multiple files, with an average of over 600 lines of code being fixed. The evaluation is conducted in isolated containers with strict CPU and memory resource constraints. [Original Link]