Language models
Several large language models (LLMs) are available in the AI chat.
LLMs (large language models) are AI models that generate human-like text by being trained on large amounts of text data. They use neural networks (e.g., transformers) to predict word probabilities.
Large Language Models differ in terms of architecture, model size, context length, the quality and breadth of training data, fine-tuning, and supported modalities. These factors influence accuracy, hallucination rates, speed, and hardware requirements, as well as data privacy and licensing aspects and suitability for tasks such as coding, translation, reasoning, or domain specialization.
LLMs are difficult to compare directly because they utilize different training data, architectures, and optimization objectives, resulting in specific strengths and weaknesses across various task domains. While standardized benchmarks such as MMLU or HELM offer a rough guide, they often fail to capture practical applicability or ethical aspects like bias and resource consumption. The choice is yours.
GPT-5-nano
OpenAI
OpenAI’s GPT-5-nano is a compact, fast AI model. Strengths include low latency, efficiency, and solid language and analysis capabilities. It is suitable for use in chatbots, automation, text processing, classification, extraction, and AI features within resource-constrained or high-throughput applications.
August 2025
OpenAI’s GPT-5.2 is a powerful AI model designed for complex language, analysis, and automation tasks. Its strengths include high quality, strong reasoning capabilities, versatility, and reliability. Potential applications range from assistants, programming, research, and content creation to support, data analysis, and sophisticated enterprise workflows.
August 2025
GPT-5.2
OpenAI
