Enterprise AI Analysis

A fundamental structural duality emerges: models like DeepSeek-R1 prioritize transparent, step-by-step reasoning (high step-accuracy) but can be fallible, while models like Qwen3 provide accurate yet opaque answers (low step-accuracy, suggesting 'shortcut learning').

This highlights a critical design challenge: balancing deliberate reasoning transparency with heuristic efficiency. For educational or safety-critical applications, transparent models are crucial. For production systems where consistency and final accuracy are paramount, other models may be preferred.

Our cross-platform validation confirms that reasoning quality is model-intrinsic rather than infrastructure-dependent. Performance variance across HPC (MareNostrum), cloud (Nebius AI Studio), and university clusters remains within 3%.

This finding democratizes rigorous AI evaluation, enabling researchers and enterprises without specialized supercomputing access to conduct scientifically valid assessments on accessible infrastructure. It ensures that model performance generalizes across diverse deployment environments.

The study reveals that non-transformer architectures like Falcon-Mamba (state-space model) achieve competitive reasoning performance, matching transformer baselines with superior consistency (0.029 std dev).

Within the Phi family, dense scaling with improved training data (Phi-4-mini) yields superior results compared to sparse MoE expansion (Phi-3.5-MoE), challenging assumptions about MoE efficiency in non-language modeling domains. This suggests that architectural design and training data quality are paramount.