The perceived performance gap between closed-source models like Claude and open-source alternatives may be skewed because benchmarks often compare raw model inference against a complete product. It is suggested that closed-model providers may employ additional behind-the-scenes processes and redactions that enhance results beyond the core architecture. Consequently, superior benchmark scores may not necessarily indicate superior training pipelines or ML techniques.