Small language models (SLMs) are increasingly adopted for machine translation due to their lower computational and deployment costs, yet a focused and systematic evaluation for English-to-Portuguese remains limited. We benchmarked dozens of SLMs (135M–20B parameters) across multiple architectures and quantization schemes (FP16, Q8_0, Q4_K_M) on two datasets: FLORES-101 (Portuguese subset, 1,012 sentences) and the multidomain OPUS-100 dataset (~10k sentences). We computed lexical and semantic metrics (BLEU, chrF, and BERTScore) and assessed statistical differences using non-parametric Friedman tests over paired sentence-level scores, followed by Wilcoxon signed-rank post-hoc comparisons with Holm correction. Normality assumptions are evaluated using the Shapiro–Wilk test. Our results strongly suggest that 8-bit quantization (Q8_0) preserves semantic quality with negligible average loss, while 4-bit quantization (Q4_K_M) reaches statistical significance in roughly half of model configurations, paired effect sizes (Cliff’s δ) remain negligible to small in magnitude, with measurable degradation concentrated in lower-capacity models. Model scale exhibits only a weak correlation with translation quality: medium-sized models can match or outperform larger ones depending on model family and pretraining. These findings highlight trade-offs between efficiency and quality and inform the design of practical English–to-Portuguese translation pipelines based on SLMs.

In recent decades, multimodal models such as CLIP have achieved significant advances in associating images and texts. However, most of these advances stem from models trained almost exclusively in English, which limits their effectiveness in other languages. This challenge is particularly relevant for Brazilian Portuguese, a language that still lacks dedicated multimodal resources and relies predominantly on automatic translations. This work investigates the performance of CLIP-based multimodal models in the task of associating images and descriptions written in Brazilian Portuguese. The analysis begins with a zero-shot scenario, in which different CLIP variants are directly evaluated on the FM30k dataset, composed of images and captions originally written in Portuguese. An additional experiment with automatic translations is also conducted to examine the impact of language on cross-modal retrieval tasks. Subsequently, fine-tuning is performed on the textual encoder of the ViT-B/32 model, keeping the visual encoder frozen, with the goal of adapting the model to the target language. The results show that models originally trained in English perform worse in Portuguese, while linguistically adapted variants, either multilingual or Portuguese-specific, achieve superior performance. The proposed fine-tuning approach was able to reduce this performance gap, leading to notable improvements. In the image-to-text scenario, the model achieved an absolute increase of 27.65 percentage points in the Accuracy@1 metric, representing a 209% relative gain over the original CLIP ViT-B/32. In the text-to-image scenario, the gain was 15.47 percentage points, amounting to an even higher 385% relative improvement, contributing to a more balanced association between images and captions.