The In-Context Learning (ICL) paradigm enables adapting LLMs without parameter tuning. This work evaluates the impact of prompt engineering on 9B models for Brazilian Portuguese sentiment classification, comparing six prompting formats (fewand zero-shot) across three linguistically diverse models (Gemma2-9B-it, Boto-9B-IT, Qwen3.5-9B) and four public datasets, anchored by a fine-tuned encoder (MSA-DistilBERT, ∼0.1B) and a 685B MoE model (DeepSeek-V3.2) under a unified protocol. Results show that all 9B models outperform the encoder and recover 71–101% of the weak-to-strong gap, with Qwen3.5-9B achieving the highest mean coverage (94.5%). Statistical tests indicate that well-defined instructions are the main driver of ICL performance, capturing most of the achievable accuracy even in zero-shot settings, while elaborate formats (roleplay, JSON schema, meta-prompting) add no consistent gain over a minimal instruction-plus-demonstrations prompt, offering practical guidance for deploying SLMs in resource-constrained PT-BR NLP scenarios.

Large Language Models (LLMs) have introduced reasoning capabilities through multi-step problem-solving processes. These models predominantly perform reasoning in English, limiting their effectiveness in other languages. This paper introduces Bode Reasoning, a Portuguese-language reasoning approach built upon fine-tuned Qwen3-4B and Qwen3-4B-Thinking models, and the Bode Reasoning Portuguese Dataset, comprising 13,961 instances from Brazilian examinations and translated datasets. Through supervised fine-tuning, the proposed approach successfully shifts the reasoning process to Brazilian Portuguese while reducing output verbosity. Experimental evaluation demonstrates that fine-tuned models generate Portuguese reasoning in 86-98.7% of outputs and achieve superior lexical alignment with reference answers. However, this specialization results in moderate mean G-Eval and accuracy degradation across diverse multiple-choice question types, highlighting inherent trade-offs in adapting multilingual reasoning models.