A foundational principle in the field of artificial intelligence asserts that there is a trade-off between a model’s explainability and its effectiveness. This trade-off significantly influences model selection for critical applications. This study presents a meta-analysis of 21 advanced NLP models from 2019 to 2023, encompassing encoder, encoder-decoder, and decoder architectures. A quantitative explainability framework was developed, grounded in architectural features, parameter efficiency, and the availability of interpretability tools. Our analysis revealed no significant correlation between explainability and performance across architectures, which contradicts common assumptions (Spearman’s rho $mathbf { xi } _ { 1 } = mathbf { xi } _ { – } 0 . 1 6 0$ , $mathrm { p } = 0 . 4 8 9 )$ ). The degree to which a model can be ex-plained is primarily predicted by the model’s intricacy $( rho =$ $- 0 . 9 5 1$ , $p < 0 . 0 0 1 )$ , though the model’s architectural family moderates this effect. Encoder-based models effectively circumvent the trade-off by achieving higher levels of ex-plainability without compromising performance. These results demonstrate that architectural design, rather than mere performance optimization, signifi-cantly influences interpretability. We hereby propose a formal explainability evaluation method and provide evidence-based recommendations for selecting models for specific use cases. Our contribution to the expanding corpus of research on interpretable AI challenges the prevailing assumption that performance and explainability are inherently incompatible. Furthermore, it offers practical guidance for developing transparent, high-performing natural language processing (NLP) systems.