Instruction-following is a core capability of Large Language Models (LLMs), as they must reliably comply with instructions constraints before being able to be evaluated on downstream tasks. In this scenario, instruction-following benchmarks for low-resource languages remain limited, particularly for Portuguese, where existing evaluations are mostly based on translated English datasets and fail to capture language specific linguistic phenomena. We introduce Portuguese-IFEval, a benchmark for evaluating instruction following in Portuguese built through semantic regionalization. The benchmark contains a total of 1236 instructions across 535 prompts, each prompt have up to three verifiable instructions, combining adapted instructions with Portuguese-specific orthographic, grammatical, and discourse-level constraints. We evaluate 25 proprietary, open-source, and Portuguese-tuned LLMs under a unified protocol, establishing a comprehensive benchmark for Portuguese instruction-following evaluation. Our results show that, while English and Spanish remain the strongest-performing languages, Portuguese still presents substantial challenges, which are effectively exposed by our benchmark. Our code is available at: https://github.com/gap-ufg/Portuguese-IFEval.