Personal AI assistants are conversational, multimodal systems and integrated assistants that help individuals and organizations with writing, research, coding, scheduling, and task automation. By 2026, the category centers on models that combine natural language, images, and structured data with connectors into apps and corporate knowledge bases, enabling productivity gains across personal and enterprise workflows. This comparison focuses on product and platform differences: Google’s Gemini family (multimodal generative models available via Google AI APIs, AI Studio and Vertex AI) emphasizes model variety, cloud-native deployment, and deep integration with Google Cloud services. OpenAI’s ChatGPT (and ChatGPT Enterprise) is built around conversational fluency, developer APIs, and ecosystem extensions. Anthropic’s Claude family targets safe, research-oriented conversational assistants tuned for analysis and long-form reasoning. Microsoft 365 Copilot embeds assistant capabilities directly into Office apps to accelerate document creation, data analysis and collaboration. Perplexity AI prioritizes web-grounded answers with citations for real-time research. Notion offers a block-based, AI-enabled workspace that combines docs, knowledge bases and automation. Cohere and IBM watsonx Assistant serve enterprise customers with private, customizable LLMs, retrieval and orchestration tools for secure assistants; Vertex AI is the underlying Google Cloud platform for model training, fine-tuning and deployment. Key decision drivers today are multimodal capability, real-time web grounding and citation, integration with personal and enterprise data, privacy/governance controls, and developer tooling for customization and orchestration. As organizations embed assistants into workflows, trade-offs between latency, costs, data residency and safety shape adoption. This comparison helps buyers and developers choose the right assistant based on use case, integration needs, and governance requirements.