GLM-5.3 Edges GPT-5.6 Sol on Cost Efficiency in DeepSWE Tasks

A recent benchmark test has shown that GLM-5.3 outperforms GPT-5.6 Sol in cost-efficiency for DeepSWE tasks, despite being slower and less accurate in single-attempt scenarios.

The DeepSWE test suite, introduced in July 2026, evaluates AI models on 113 long-horizon programming tasks across multiple languages and domains. In the benchmark, GLM-5.3 demonstrated a pass@4 lead of 87.6% compared to GPT-5.6 Sol's 85.8%, while operating at half the cost per rollout ($3.99 vs. $8.37).

While GPT-5.6 Sol excels in precision-heavy fields like data modeling and protocol conformance, GLM-5.3 performs best in structured tasks such as query languages and runtime internals. By language, GLM-5.3 leads in JavaScript (90% vs. 75%) and Rust, whereas Sol outperforms in Python, Go, and TypeScript.

The findings suggest that GLM-5.3 is a compelling choice for budget-sensitive or retry-tolerant use cases, offering a safer and more affordable option for teams running batch rollouts or verifying outputs post-run. In contrast, GPT-5.6 Sol remains the premium choice for time-sensitive, precision-critical applications.