GLM-5.3 vs. GPT-5.6 Sol on DeepSWE: Cost, Coding, and Routing

This post provides a detailed comparative analysis of GLM-5.3 and GPT-5.6 Sol on the DeepSWE benchmark, focusing on pass@k metrics, cost per rollout, failure modes, and domain/language-specific performance. It quantifies the trade-offs: GLM-5.3 offers better value and reach (pass@4, coverage) at lower cost and with cleaner failure profiles (fewer regressions), while GPT-5.6 Sol leads in single-shot accuracy (pass@1), speed, and reliability, particularly in protocol conformance. The key technical contribution is the demonstration and quantification of a cascaded routing strategy (GLM-5.3 first, then GPT-5.6 Sol) that achieves 85.9% task completion at $6.61 per task, outperforming either model alone and a perfect one-shot oracle router.