Generative AI coding assistants and models now power everyday development workflows, from inline completions to autonomous agentic tasks. This topic examines how leading systems — Claude Opus 4.5 and ChatGPT as generalist/code-capable models, GitHub Copilot (IDE-integrated pair programmer with chat and agent workflows), Amazon CodeWhisperer (inline suggestions within the Amazon Q Developer experience), and specialist/code-first models like Code Llama and Stable Code — differ in capabilities and deployment trade-offs. Relevance in 2026 stems from three converging trends: wider adoption of instruction‑tuned, code‑aware LLMs; a push for local/self‑hosted and edge‑ready models to address latency, privacy, and IP concerns (e.g., Tabby, Stable Code); and the rise of agentic development environments that chain model actions into developer workflows (Warp, Blackbox.ai). Complementary tools — CodeGeeX, Bito (PR and review automation), and other codebase‑aware assistants — emphasize codebase context, review automation, and reproducible fixes. Key comparison axes include correctness and hallucination rates, context‑window and cross‑repo awareness, latency, IDE and CI/CD integrations, licensing and security constraints, and support for autonomous tasks (test generation, bug fixes, pull request automation). Practical trade‑offs matter: cloud-hosted scalable models tend to offer broader knowledge and managed safety, while self‑hosted/open models give control over data and compliance. Evaluations should therefore consider real project needs (team size, codebase sensitivity, language stack, CI integration) rather than raw model benchmarks. This topic helps developers and managers choose between off‑the‑shelf services and self‑hosted code models, and understand how these tools fit into modern development pipelines.