Kimi K2.7-Code cuts thinking tokens 30% - but practitioners say the benchmarks don't check out

Kimi K2.7-Code, a recent update to the Kimi framework, promises significant performance boosts with its 30% reduction in thinking tokens and a simplified API swap path. However, independent benchmarks have cast doubt on these claims, revealing kernel regressions and a lack of DeepSWE submission, a key indicator of performance. This raises questions about the accuracy of the benchmarks and the true performance capabilities of Kimi K2.7-Code. As developers and practitioners rely on these metrics to inform their decisions, the discrepancy between claimed and actual performance has significant implications for the adoption and optimization of the Kimi framework. The issue highlights the need for more rigorous testing and validation procedures to ensure that performance claims are backed by concrete evidence.