This research investigates the impact of constrained decoding on Large Language Model (LLM) performance, challenging the prevailing assumption that forcing specific output formats inherently degrades reasoning capabilities. The authors propose a taxonomy consisting of three distinct constraint types: format constraints for structural enforcement, validity constraints to mask invalid answers, and reasoning constraints to restrict specific reasoning trajectories. Through causal ablations conducted across four model families and eleven open-source models, the study demonstrates that perceived performance drops are frequently artifacts of non-equivalent prompting and output formats rather than the decoding-level masking itself. Theoretically, the paper argues that appropriate constraints can actually enhance performance by pruning invalid paths from the output space. Specifically, the findings indicate that validity constraints improve both reliability and answer extractability by eliminating incorrect responses, while reasoning constraints can bolster accuracy when they successfully enforce beneficial computational trajectories. Ultimately, the study concludes that the effect of constrained decoding is highly contingent upon the specific constraint type and the nature of the output space being removed, suggesting that well-designed constraints can be beneficial tools for improving LLM utility rather than being purely detrimental to model intelligence.