Last Updated on January 3, 2026
Introduction
Private AI for Word is becoming the standard for technical professionals who require logic writing without the security risks of cloud-based LLMs. To achieve a true private Microsoft Copilot alternative for mathematical and analytical drafting, users are increasingly turning to compact, reasoning-heavy models. This local-first strategy is at the core of our comprehensive guide to Private AI for Word, where we prioritize local performance and 100% data ownership.
As part of our evaluation of local LLMs for GPTLocalhost, we have tested the IBM Granite 3.3 and Microsoft Phi-4 models. These models represent the cutting edge of “small-but-mighty” AI, delivering impressive reasoning for math, logic, and structured data directly within your Microsoft Word locally.
Watch: Math Reasoning Performance Demo
This demonstration highlights how Granite-3.3-8B and Phi-4-Mini-Reasoning integrate with Microsoft Word to handle multi-step calculations and technical problem-solving.
Our demo illustrates how seamless local inference can be for solving math problems. For more creative ideas on using private GPT models in Microsoft Word, please visit the additional demos available on our @GPTLocalhost channel.
Technical Profile: Why Granite-3.3-8B–Instruct? (Download Size: 4.94 GB)
According to IBM’s model card, Granite-3.3-8B-Instruct is a 8-billion parameter 128K context length language model fine-tuned for improved reasoning and instruction-following capabilities. It supports structured reasoning through and tags, providing clear separation between internal thoughts and final outputs. The model has been trained on a carefully balanced combination of permissively licensed data and curated synthetic tasks.
- Multilingual & Versatile Utility: The model supports 12 major languages—including English, Arabic, and Japanese—and excels in a wide array of tasks such as RAG, function-calling, and code generation, with the flexibility to be fine-tuned for additional languages.
- Enhanced Reasoning Performance: Built on top of Granite-3.3-8B-Base, the model delivers significant gains on benchmarks for measuring generic performance and improvements in mathematics. This model is designed to handle general instruction-following tasks and can be integrated into AI assistants across various domains, including business applications.
Technical Profile: Why Phi-4-Mini-Reasoning? (Download Size:2.49 GB)
Phi-4-Mini-Reasoning is a lightweight open model that balances efficiency with advanced reasoning ability. Specifically engineered for memory-constrained environments and latency-bound scenarios, this model excels at multi-step mathematical problem-solving and symbolic computation. According to this graph, despite its compact size, it demonstrates remarkable breakthroughs in deep analytical thinking, outperforming many models more than twice its size on benchmarks like GPQA Diamond and Math-500, making it an ideal choice for high-speed, local reasoning within Microsoft Word.
- Logic-Intensive Problem Solving: Designed for formal proofs and symbolic computation, the model balances high-quality reasoning with cost-effective deployment. Its efficient architecture makes it perfectly suited for educational applications, embedded tutoring, and lightweight edge systems where multi-step problem solving is required.
- Large Context and Robust Alignment: Supporting a 128K token context length, the model can process and reason over extensive mathematical proofs and long-form documents. It leverages advanced fine-tuning techniques on high-quality synthetic math datasets, ensuring reliable and robust performance for complex use cases.
Deployment Reminders: Check VRAM Size
These two models were evaluated on a Mac M1 Max. Due to their relatively small download sizes, these reasoning-focused models can generally run smoothly on consumer-grade machines equipped with a GPU or Apple Silicon. For the two models tested, 8GB of VRAM or more should be sufficient. If a model exceeds your available VRAM, low-bit quantized variants offer a practical alternative.