AI coding assistants now span inline completions, chat-driven help, agentic workflows, and self‑hosted model deployments. This topic compares leading approaches — Anthropic’s Claude Code and GPT‑based systems vs platform solutions like GitHub Copilot — and shows how developers choose between accuracy, latency, privacy, and integration. As of 2026, the space centers on three trends: deeper IDE integration and agentic automation (Copilot, Cursor, Windsurf/Windsurf Editor, JetBrains AI Assistant), enterprise governance and private deployments (Tabnine, enterprise variants of Copilot and CodeWhisperer), and specialized/edge models for fast, private inference (Stable Code and other instruction‑tuned code LLMs). Tools such as Refraction target repetitive engineering work — tests, docs, refactors — while platforms like Cursor and Windsurf embed agents across editor, CLI, and web workflows to keep developers “in flow.” Claude Code and GPT‑based models represent two architectural choices: Anthropic’s models emphasize instruction‑tuning and safety guardrails, while GPT families power many chat and assistant experiences across third‑party tools. GitHub Copilot ties model-driven completions and Copilot Chat tightly to GitHub and editor workflows. Amazon CodeWhisperer (now converging into Amazon Q Developer) and JetBrains AI Assistant focus on seamless in‑IDE assistance. Tabnine and Stable Code address privacy, compliance, and on‑premises needs. Choosing among them depends on required context window, language support, autonomy (agentic tasks), privacy constraints, and cost/latency. Evaluations should include code accuracy, hallucination rates, license/telemetry policies, and how well the assistant integrates into CI/CD and testing pipelines. This comparison helps engineers and teams match capabilities to workflow, risk tolerance, and governance needs.