AI‑powered smart glasses in 2026 bring together edge vision, low‑latency multimodal assistants, and cloud services to deliver live translation, object recognition, contextual answers and workflow integrations. This topic compares major approaches from Google, Meta, Samsung and eyewear-focused vendors like Warby Parker while situating them in two key categories: Edge AI Vision Platforms and Personal AI Assistants. Relevance (2025‑12‑11): advances in efficient multimodal models, on‑device inference, and standardized developer APIs mean glasses can run useful vision+language features without constant cloud roundtrips—addressing latency, connectivity and privacy concerns. At the same time, cloud platforms remain essential for heavyweight tasks, model tuning and enterprise integrations, so device OS and cloud stack choices determine real‑world capability and enterprise suitability. Key tools and roles: Google’s Gemini family supplies multimodal generative models and developer APIs for vision+language features; Vertex AI supports model lifecycle, deployment and cloud/edge orchestration. Anthropic’s Claude provides conversational assistant primitives for safe dialogue; Cohere offers private, customizable LLMs for enterprise use cases. Perplexity delivers web‑grounded, sourced answers for research‑style queries; DeepL enables high‑quality translation and captioning. IBM watsonx Assistant supports no‑code and developer agents for enterprise workflows, while Microsoft 365 Copilot links assistant capabilities into productivity apps. Practical tradeoffs: expect variance in OS openness, on‑device model support, developer ecosystems, and enterprise controls. The most useful comparisons focus on latency, privacy/edge processing, supported assistant stacks, translation accuracy, and how each vendor integrates cloud services for updates, security and business workflows.