Kimi K3 vs GPT-5.6 Sol on DeepSWE: Cost, Coding, and Routing

The comparison between Kimi K3 and GPT-5.6 Sol on the DeepSWE benchmark highlights their distinct strengths and weaknesses, with Kimi K3 excelling in cost-effectiveness and pass@k metrics while GPT-5.6 Sol demonstrates higher reliability and single-attempt quality. GPT-5.6 Sol leads slightly in pass@1 with a 72.7% success rate compared to Kimi K3's 68.5%, yet Kimi K3 surpasses Sol in pass@2 and pass@4, at a considerably lower cost per rollout (\$4.65 compared to Sol's \$8.37). Kimi K3 achieves 14.7 solved tasks per \$100, making it about 2.8 times more cost-efficient than Sol. Despite Sol's steadiness and higher reliability (84.5% with tasks solved four-for-four), Kimi K3 offers broader coverage with 89.4% of tasks solved at least once across four tries. The two models exhibit a 0.46 correlation in task performance, indicating they succeed and fail differently, making a combined routing strategy between them advantageous. This approach, using a Kimi-first cascade with escalation to Sol when necessary, covers 108 of 113 tasks and achieves an 85.6% success rate while maintaining cost efficiency. The analysis suggests that the optimal strategy for many teams is to leverage both models, exploiting their complementary strengths.

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