Last Updated on March 2, 2026
Introduction
Private AI for Word has become the primary solution for professionals requiring high-level data security who are increasingly moving away from cloud-based assistants. To achieve a true private Microsoft Copilot alternative on your intranet, users must deploy a Local LLM directly on your own hardware to eliminate the risks associated with third-party data processing. By running GPTLocalhost as a local Word Add-in, you can deploy this optimized 24B model directly on your hardware. This focus on privacy is a core pillar of our Local LLM Benchmarks for Microsoft Word.
This following demonstration highlights how Mistral Small 3 handles complex document summarization. This instruction-tuned model performs exceptionally well, competing with open-weight models up to three times its size and even with the proprietary GPT4o-mini model. This is evident across benchmarks for Code, Math, General Knowledge, and Instruction Following.
Watch as it analyzes a long-form document and generates a structured, high-fidelity summary directly inside Microsoft Word without any data leaving the local machine.
You can watch this quick demo video to see this in action. The demo is powered by GPTLocalhost, which offers the same core features for individual use. LocPilot in Word is the Intranet edition of GPTLocalhost designed for enterprise users and team collaboration. For a quick demo of LocPilot, please click here.
For more creative uses of local and private LLMs in Microsoft Word, explore additional demos available on our channel at @LocPilot.
Technical Profile: Why Mistral Small 3 for Word? (Download Size: 14.33 GB)
Mistral Small 3 is a pre-trained, instruction-tuned model designed to cover the “80%” of generative AI use cases—delivering strong language understanding and instruction-following capabilities. It is a great versatile model for tasks such as long document processing, low-latency applications, and summarization, etc.
- Long-Context Window: Mistral Small 3.1 features a context window of up to 128,000 tokens. This allows it to summarize entire books, lengthy research papers, or complex legal contracts without needing to chunk the text.
- Frontier performance: Achieve closed-source-level results with the transparency and control of open-source models.
- Multilingual: Build applications that understand text and complex logic across 40+ native languages.
- Scalable efficiency: From 3B to 675B parameters, choose the model that fits your needs, from edge devices to enterprise workflows.
Deployment Reminders: Running Mistral Small 3 Locally
When quantized, Mistral Small 3 can be run privately on a single RTX 4090 or a Mac with 32GB RAM. Our evaluation was conducted on a Mac M1 Max with 64GB of RAM. Although the inference speed is not especially fast, it remains practical and acceptable given that this is a mid-sized model.
According to Mistral’s post, the model can be fine-tuned for specific domains, enabling the creation of highly accurate subject-matter experts. This capability is especially valuable in areas such as legal advice, medical diagnostics, and technical support, where deep domain knowledge is critical. It is also worth noting that, following our evaluation, several newer models have since been released, as listed below. Interested users are encouraged to test them as well.
- Mistral Small 3.1 (25.03): An updated version that added enhanced long context and state-of-the-art vision understanding capabilities.
- Mistral Small 3.2 (25.06): The version improves instruction following, reduces repetition errors, and strengthens function calling capabilities.
- Mistral Small Creative (25.12) : An experimental “Labs” model specialized for creative writing and character interaction.
The Local Advantage
Running your LLM models locally via LocPilot ensures:
- Air-Gapped Security: Operate entirely within your intranet — no external connections.
- Cost Savings: Eliminate subscription fees for the entire team — no ongoing costs.
- Model Flexibility: Easily host and switch models to suit your use cases — no vendor lock-in.