GitHub Copilot vs. Qoder vs. StarCoder vs. python-sql Comparison Chart

GitHub Copilot is an AI-powered coding companion that helps developers write, debug, and refactor code faster than ever. Built directly into popular IDEs like VS Code, JetBrains, and Visual Studio, Copilot suggests context-aware code completions and automates repetitive tasks. With Agent Mode, Copilot can plan, write, test, and even submit pull requests—acting like an onboarded team member that understands your repositories. Developers can toggle between advanced models such as GPT-5, Claude Opus 4.1, and Gemini 2.5 Pro to balance speed and depth. Its Copilot Chat and Spaces features bring code, documentation, and project notes together for richer, team-tailored insights. Whether you’re building solo or leading an enterprise project, Copilot enhances productivity and creativity across the entire software lifecycle. The GitHub Copilot coding agent streamlines development by automating repetitive and time-consuming tasks, allowing developers to focus on more complex coding.

StarCoder and StarCoderBase are Large Language Models for Code (Code LLMs) trained on permissively licensed data from GitHub, including from 80+ programming languages, Git commits, GitHub issues, and Jupyter notebooks. Similar to LLaMA, we trained a ~15B parameter model for 1 trillion tokens. We fine-tuned StarCoderBase model for 35B Python tokens, resulting in a new model that we call StarCoder. We found that StarCoderBase outperforms existing open Code LLMs on popular programming benchmarks and matches or surpasses closed models such as code-cushman-001 from OpenAI (the original Codex model that powered early versions of GitHub Copilot). With a context length of over 8,000 tokens, the StarCoder models can process more input than any other open LLM, enabling a wide range of interesting applications. For example, by prompting the StarCoder models with a series of dialogues, we enabled them to act as a technical assistant.