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

A comparison of GLM-5.3 and GPT-5.6 Sol on 113 DeepSWE software-engineering tasks across four trials each finds that Sol leads on first-attempt success, reliability, and speed, achieving 72.7% pass@1 versus GLM-5.3’s 69.0% while averaging 19 rather than 35 minutes per rollout. GLM-5.3 costs less than half as much per rollout at $3.99 versus $8.37, delivers more estimated solves per $100, and overtakes Sol when retries are allowed, tying at pass@2 and leading 87.6% to 85.8% at pass@4. The models have distinct strengths: GLM-5.3 performs particularly well on JavaScript, Rust, query and configuration languages, runtime internals, and stateful reactivity, while Sol leads in Python, Go, TypeScript, protocol conformance, and several systems-oriented domains. Sol’s failures more often introduce regressions in existing tests, whereas GLM-5.3 more frequently produces near misses without breaking the baseline suite. Because their task-level outcomes show limited correlation and together cover 106 of 113 tasks, the analysis recommends a verifier-gated cascade that tries GLM-5.3 first and escalates failures to Sol, estimating 85.9% task coverage at $6.61 per solved task, though the findings are limited to the stated DeepSWE records, scoring rules, and assumptions about independent attempts and test-based verification.

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