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

Sol wins the first try, GLM-5.3 wins the rest at half the price, and the cascade beats both: 85.9% at \$6.61 a task.

40+ Models Chosen for Production...40+ Models Chosen for Production...40+ Models Chosen for Production...

Don't pick one. Run GLM-5.3 first, escalate to GPT-5.6 Sol when the tests fail. That cascade solves 85.9% of DeepSWE tasks at \$6.61 each. Sol alone solves 72.7% at \$8.37. Thirteen points better, 21% cheaper.

We ran GLM-5.3 (max) against GPT-5.6 Sol (max) on all 113 DeepSWE tasks, four trials each, from the published per-trial records: 904 rollouts in total, 452 per side. Sol is the precision flagship. GLM-5.3 is the open-weight challenger that closed the gap. Every figure below comes from this run, so it can differ from other public GLM-5.3 vs. GPT-5.6 Sol scorecards.

GPT-5.6 Sol still holds the single-shot crown on DeepSWE, a benchmark that tests a model's software engineering ability across many task types and programming languages. GLM-5.3 arrives less than four points behind it at half the price and pulls ahead the moment you allow more than one attempt. This is the closest the open tier has come to the frontier, and the question worth answering is what Sol's remaining premium actually buys.