Our engineering team compared five leading AI code assistants—Cursor, Junie, AWS Q, Copilot, and ChatGPT—to understand how they perform in real engineering workflows.
As a development partner, Gritmind meets clients where they are and helps them shape their future ways of working. Each client uses a unique technology stack and development workflow. By maintaining expertise across major IDEs, frameworks, and AI code assistants—including Cursor, Junie, AWS Q, Copilot, and ChatGPT—we adapt quickly to client environments and identify opportunities to modernize how software teams build and deliver products.
Our evaluation covers responsiveness, accuracy, autonomy, and ease of integration based on hands-on testing as of August 2025. Code assistants are rapidly evolving and we'll come back periodically to update our analysis based on more recent experiences. Here is how each tool performed in real engineering use:
As a development partner, Gritmind meets clients where they are and helps them shape their future ways of working. Each client uses a unique technology stack and development workflow. By maintaining expertise across major IDEs, frameworks, and AI code assistants—including Cursor, Junie, AWS Q, Copilot, and ChatGPT—we adapt quickly to client environments and identify opportunities to modernize how software teams build and deliver products.
Our evaluation covers responsiveness, accuracy, autonomy, and ease of integration based on hands-on testing as of August 2025. Code assistants are rapidly evolving and we'll come back periodically to update our analysis based on more recent experiences. Here is how each tool performed in real engineering use:
AI Code Assistant Comparison
| Cursor | Junie | AWS Q | Copilot | ChatGPT | |
|---|---|---|---|---|---|
| UX, ease of use and seamless integration | Comes bundled with its own IDE, which can be a drawback for teams with an established setup—but few developers complain once they switch | Built by JetBrains, it integrates best with their IDEs—which works well if your team is already in that ecosystem | Works as a plugin for major IDEs—a potential plus—but integrations still feel immature and can be buggy. | Integrates well with popular IDEs, making it easy to adopt within existing workflows. | Uses its own cloud-based workspace, making it an awkward fit for team environments. |
| Responsiveness | Delivers strong performance thanks to its native integration—responses are quick and feel immediate within the IDE | Initial responsiveness was uneven but has improved with recent updates—still worth monitoring before full adoption | Significantly slower to respond than other options—simple tasks are often faster to complete manually | Produces responses quickly and seamlessly, making it one of the smoother real-time experiences | Among the most responsive options—handles prompts and code generation with minimal delay |
| Accuracy | Autocomplete is consistently accurate—relevant, context-aware, and rarely needs revision | Autocomplete is slow and rarely helpful | Autocomplete tends to distract more than it helps | Autocomplete delivers the most accurate suggestions overall—often anticipating developer intent and completing code naturally | Autocomplete generates highly accurate code in non-agent mode but can be overly verbose. Better results can be achieved with clear, structured prompts |
| Autonomy | Can work across the full codebase and update relevant files. Supports user-approved actions and partial updates, giving developers control without sacrificing speed | Lacks checkpoint features—developers must commit before use, increasing the risk of unwanted code changes. | Similar to Junie, it lacks checkpoint features and requires committing before use—raising the risk of overwriting code. | Still requires developers to review and approve all generated changes—offers partial autonomy, not full automation. | Provides no in-environment autonomy—users must manually transfer and apply generated code. |
The takeaway
No single AI code assistant fits every team—but two stand out today. Cursor and Copilot currently lead the pack for speed, reliability, and overall developer experience. The others—Junie, AWS Q, and ChatGPT—offer advantages in areas like documentation, research, and enterprise integration.
At Gritmind, we help teams cut through the noise, test these tools in real-world environments, and identify where AI can deliver meaningful leverage. When paired with a consistent software delivery process and well structured input, these assistants make developers more productive and can improve overall codebase quality.
At Gritmind, we help teams cut through the noise, test these tools in real-world environments, and identify where AI can deliver meaningful leverage. When paired with a consistent software delivery process and well structured input, these assistants make developers more productive and can improve overall codebase quality.