Best Large Language Models

Large Language Models (LLMs) in Vertex AI enable businesses to perform complex natural language processing tasks such as text generation, summarization, and sentiment analysis. These models, powered by massive datasets and cutting-edge techniques, can understand context and generate human-like responses. Vertex AI offers scalable solutions for training, fine-tuning, and deploying LLMs to meet business needs. New customers receive $300 in free credits, allowing them to explore the potential of LLMs in their applications. With these models, businesses can enhance their AI-driven text-based services and improve customer interactions.

LM-Kit.NET lets C# and VB.NET developers integrate large and small language models for natural language understanding, text generation, multi-turn dialogue, and low-latency on-device inference, while its vision language models add image analysis and captioning, its embedding models turn text into vectors for fast semantic search, and its LM-Lit catalog lists every state-of-the-art model with continuous updates, all in one efficient toolkit that stays inside your codebase without revealing any AI origin to the user.

NuExtract is a large language model specialized in extracting structured information from documents of any format, including raw text, scanned images, PDFs, PowerPoints, spreadsheets, and more, supporting over a dozen languages and mixed‑language inputs. It delivers JSON‑formatted output that faithfully follows user‑defined templates, with built‑in verification and null‑value handling to minimize hallucinations. Users define extraction tasks by creating a template, either by describing the desired fields or importing existing schemas—and can improve accuracy by adding document, output examples in the example set. The NuExtract Platform provides an intuitive workspace for designing templates, testing extractions in a playground, managing teaching examples, and fine‑tuning settings such as model temperature and document rasterization DPI. Once validated, projects can be deployed via a RESTful API endpoint that processes documents in real time.