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Zhipu GLM-5.2 Tops DeepSWE Open Source Rankings: Solves 44% of Complex Development Tasks, Outperforms Main Closed-Source Models
The average cost per task for GLM-5.2 is $3.92, slightly higher than Kimi K2.7 Code's $2.82, but its success rate surpasses the performance of several mainstream closed-source models under specific thinking 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 evaluator Datacurve, specifically tests the AI agent's ability to solve long tasks. The test includes 113 real programming problems covering five languages. Unlike traditional tests that modify only a single piece of code, DeepSWE requires AI to collaboratively modify multiple files, with an average of over 600 lines of code repaired. The evaluation runs in isolated containers with strict CPU and memory resource limitations.