Together AI benchmarked DeepSeek V4 Pro 0813 against OpenAI's GPT-5.6 Sol on DeepSWE, a software engineering benchmark across task types and languages. Sol wins single-shot: 72.7% pass@1 vs Pro's 62.8%. But at pass@4 (four retries), Pro pulls ahead: 88.5% vs 85.8%. The critical difference: DeepSeek costs $0.24 per rollout, while Sol costs $8.37 per rollout—a 35x gap.
At scale, Pro delivers 260 task solves per $100 spent versus Sol's 9. Sol is faster (17 min vs 35 min average, 53 steps vs 146) and more precise per attempt. Pro is reliable at lower cost and wins on Rust tasks; Sol dominates Python/Go. Failure modes differ: Sol breaks existing test suites in 20% of failures (GPT-family regression signature); Pro is more conservative at 11%.
The optimal strategy is a cascade: run Pro first, escalate to Sol on test failures. This hybrid approach solves 83% of DeepSWE tasks at $3.35 per task—10 percentage points above Sol-only at 40% of Sol's unit cost. For teams with latency budgets, Sol is premium; for cost-constrained inference or batch work, Pro-first cascading flips the economics.