Sabi'a-4 Technical Report
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
This technical report presents Sabi'a-4 and Sabiazinho-4, a new generation of Portuguese language models with a focus on Brazilian Portuguese language. The models were developed through a four-stage training pipeline: continued pre-training on Portuguese and Brazilian legal corpora, long-context extension to 128K tokens, supervised fine-tuning on instruction data spanning chat, code, legal tasks, and function calling, and preference alignment. We evaluate the models on six benchmark categories: conversational capabilities in Brazilian Portuguese, knowledge of Brazilian legislation, long-context understanding, instruction following, standardized exams, and agentic capabilities including tool use and web navigation. Results show that Sabi'a-4 and Sabiazinho-4 achieve a favorable cost-performance trade-off compared to other models, positioning them in the upper-left region of the pricing-accuracy chart. The models show improvements over previous generations in legal document drafting, multi-turn dialogue quality, and agentic task completion.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 7 of its 12 references that are in the map. First posted on arXiv 10 Mar 2026.
Builds on
7 of 12 references.
Closest ancestors · 1
Same direction, newest first
- 2025APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay4 Apr
Other references · 6
- 2024Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Followingcited by 94
- 2024Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queriescited by 85
- 2025Automatic Legal Writing Evaluation of LLMscited by 11
- 2025Building High-Quality Datasets for Portuguese LLMs: From Common Crawl Snapshots to Industrial-Grade Corporacited by 7
- 2024Juru: Legal Brazilian Large Language Model from Reputable Sourcescited by 5
- 2025Comparing Knowledge Injection Methods for LLMs in a Low-Resource Regimecited by 2
Built on by
Citing papers per year: 2026: 5.
5 papers.
- 2026Beyond English benchmarks: clinical llm evaluation in Brazilian Portuguesecited by 1
- 2026CAPITU: A Benchmark for Evaluating Instruction-Following in Brazilian Portuguese with Literary Contextcited by 0
- 2026MARCA: A Checklist-Based Benchmark for Multilingual Web Searchcited by 0
- 2026BLUEX v2: Benchmarking LLMs on Open-Ended Questions from Brazilian University Entrance Examscited by 0
- 2026Eleven Years of BRACIS: A Meta-Scientific Study of the Brazilian Conference on Intelligent Systemscited by 0
- 2026Eleven Years of BRACIS: A Meta-Scientific Study of the Brazilian Conference on Intelligent Systems26 Jul 2026
- 2026BLUEX v2: Benchmarking LLMs on Open-Ended Questions from Brazilian University Entrance Exams21 Jun 2026
- 2026Beyond English benchmarks: clinical llm evaluation in Brazilian Portuguese5 Jun 2026
- 2026MARCA: A Checklist-Based Benchmark for Multilingual Web Search15 Apr 2026
- 2026CAPITU: A Benchmark for Evaluating Instruction-Following in Brazilian Portuguese with Literary Context23 Mar 2026
Similar, not cited
Close in wording, no citation either way
- 2026Tucano 2 Cool: Better Open Source LLMs for Portuguese
- 2024LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain
- 2026OneMillion-Bench: How Far are Language Agents from Human Experts?
- 2025Challenging the Abilities of Large Language Models in Italian: a Community Initiative
- 2025Alvorada-Bench: Can Language Models Solve Brazilian University Entrance Exams?
- 2025MEDAL: A Framework for Benchmarking LLMs as Multilingual Open-Domain Dialogue Evaluators
- 2026A Few Good Clauses: Comparing LLMs vs Domain-Trained Small Language Models on Structured Contract Extraction
- 2025AI agents may be worth the hype but not the resources (yet): An initial exploration of machine translation quality and costs in three language pairs in the legal and news domains