Brazil’s ENEM, a high-stakes assessment determining university admission for millions of students annually, creates an immense evaluation burden where human raters process hundreds of essays daily. Automated Essay Scoring (AES) offers a potential solution, yet Portuguese-language systems remain understudied due to fragmented datasets and the complexity of ENEM’s multi-trait rubric. This work investigated cross-prompt, trait-specific essay scoring using a corpus of 385 essays across 38 prompts, where models evaluated essays on unseen prompts across five traits scored on a six-point ordinal scale. We compared three model classes: feature-based methods (72 features), encoder-only transformers (109M–1.5B parameters), and decoder architectures (2.4B–671B parameters) with fine-tuned and zero-shot configurations. Experiments under varying information access and rubric conditioning revealed that no single approach serves all evaluation needs: encoder models excel at mechanical traits (fluency, cohesion) despite context limitations; decoder models achieve superior performance on argumentation (QWK 0.73) and writing style (QWK 0.60) when provided full context; and language-specific pretraining benefits only surface-level features without improving complex reasoning. Best-performing models achieved QWK scores of 0.60–0.73. Gaps to oracle bounds ranged from 0.15 (argumentation) to 0.29 (writing style), with the largest disparities in writing style and persuasiveness.