It’s not always clear where AI assistants fit in the workflow- as well as how much they really cost and whether they actually make projects easier.
The bigger issue is that developers want smarter choices, not just more code.
In the upcoming comparison, we’ll talk about both Jetbrains and Copilot – the best Best AI coding assistants around, and how good they are for solving these issues.
Simply put; JetBrains AI keeps edits and docs consistent, while Copilot throws fast suggestions your way. You decide between control or speed.
Overview of JetBrains AI Assistant
JetBrains AI Assistant runs in the IDE and uses the IDE’s understanding of your code.
It looks at symbols, files, and inspections to suggest changes. You trigger most actions yourself, so it doesn’t constantly interrupt while typing.
Key Capabilities
- Inline code completion with multiple suggestions for lines or blocks
- AI chat tab powered by Google Gemini, OpenAI, and Anthropic Claude
- Explain, simplify, analyze, and refactor code
- Generate tests and documentation
- Next edit suggestions and smart renaming for consistent names
- Smart Apply to insert code from chat
- Convert code to another language
It works with the IDE’s PSI trees, inspections, refactoring tools, and project files, not just text.
What Is JetBrains AI Assistant Best For?
JetBrains AI Assistant is best for working inside an existing project without losing context.
It helps you refactor safely, catches related changes when you add features, and takes care of basic tests and documentation so you can keep moving.
Overview of GitHub Copilot
GitHub Copilot works in your editor, watching your file and nearby code to suggest what to type next.
You can accept, change, or ignore suggestions. It can fill boilerplate or repetitive code fast and can help with new files or prototypes.
Key Capabilities
- Inline code completion and auto-completion for lines or blocks
- Code prediction and generation based on surrounding code
- Explain code and generate alternative snippets
- Generate tests for common cases
- Generate commit and pull request messages
It works similarly across editors. Suggestions sometimes even give a few options at once, which helps when filling repetitive patterns or experimenting.
What Is GitHub Copilot Best For?
GitHub Copilot is best for speed. It’s great at knocking out boilerplate, spinning up new files or quick prototypes, offering inline alternatives as you type, and even helping wrap things up with commit messages.
IDE Integration and Workflow: How the AI Works
The two assistants handle your editor differently. JetBrains AI uses the IDE’s understanding of your project. Copilot predicts code in the file you’re working on.
JetBrains AI Assistant
JetBrains AI Assistant runs inside IntelliJ IDEA and PyCharm. Its suggestions fit naturally into your workflow, so you rarely have to second-guess them.
GitHub Copilot
GitHub Copilot works in VS Code and Visual Studio. It offers quick, on-the-fly suggestions that speed up prototypes, though you still need to keep an eye on bigger projects.
JetBrains AI Assistant vs GitHub Copilot: Feature Comparison
With how differently they work to help projects get done faster, the resulting edits across multiple files, tests, or logic consistency play out differently.
Code Completion And Suggestions
Win: JetBrains AI for reduced chances of errors.
JetBrains offers inline completion and multiple predictions that follow the project’s structure and coding style. It nudges related code areas along, so changes feel consistent and in tune with what’s already there.
Copilot throws up suggestions quickly, usually several options per line or block. It’s handy for drafting new logic or experimenting in small chunks, keeping the flow moving without much pause.
Context Awareness
Win: JetBrains AI for context awareness
Project symbols, call graphs, and IDE inspections are right up Jetbrains’ alley. That means when you refactor or add features, the edits will stay coherent, and files feel connected rather than scattered.
Copilot mostly looks at the active file and nearby lines. It can explain code and help with local tweaks, but broader continuity relies on keeping an eye on things yourself.
Language Support And IDE Compatibility
Win: Copilot for flexibility and cross-tool support
JetBrains AI Assistant works deeply in JetBrains IDEs and respects inspections, refactoring rules, and conventions. There are language-aware suggestions that help edits match the structure of the project, which reduces small mistakes even further along the way.
Copilot runs in a wide range of editors and handles multiple languages. It’s flexible if you jump between projects or tools, though it doesn’t always check everything that a full IDE would.
Tests, Documentation, And Long-term Reliability
Win: JetBrains AI for reliable tests and documentation
With JetBrains AI Assistant, you get unit tests, edge case coverage, and inline docs that match your project. It keeps results uniform so you don’t redo work.
Copilot spins out tests, docstrings, and summaries quickly within the current scope. It’s great for short-term work, though bigger systems or multi-file logic may need a bit of double-checking.
Soft Edges And Quirks
Win: Copilot for speed and rapid experimentation
JetBrains AI Assistant sometimes pauses just enough to gather context, making multi-file changes safer and more predictable. The approach is meant more for consistency over instant speed.
Copilot reacts right away, which is nice when you want fast drafts. It often nails things, though broader logic or refactoring might still need a look.
Pricing and Licensing
JetBrains AI Assistant comes in Free, Pro, Ultimate, and Enterprise.
Cloud use and extra features go up with each plan, but using local AI or basic code completion doesn’t eat into credits.
Teams get extra tools for managing users, and Enterprise adds security and admin options. There’s a trial to try things out, and you can buy more cloud credits if needed.
Copilot is separate from regular GitHub plans. You can start free, or pay based on how much you use it and which models you pick.
Organizations handle seats and overages, and premium requests unlock extra chat and agent features. Billing is monthly or yearly, easy to follow, but going over your limits costs extra.
In this regard – JetBrains AI Assistant wins again. Its pricing will work the best with your IDE setup and the way you code. Copilot is simple and flexible, but it shines most when you use it a lot for bigger projects.
How Both AI Assistants Affect Code Quality
JetBrains AI Assistant works with the IDE to make suggestions that actually fit your project. Refactoring or adding features usually goes smoothly, and fewer little errors sneak in.
Copilot is fast and more active, throwing out lines and blocks as you type. It’s great for experimenting or new code, but you still need to check bigger projects for consistency.
Both have their perks. JetBrains keeps things tidy, while Copilot keeps things moving. Knowing how they behave helps you avoid extra work and stay in flow.
Which AI Assistant Supports Testing and TDD/BDD?
JetBrains AI Assistant can create unit tests that match your project’s style and cover edge cases. It works with TDD or BDD and updates as your code changes.
Copilot spins out test stubs fast. They usually need tweaks to handle special cases or match conventions, but they give something to run immediately, which is handy for quick experiments or new modules.
The approaches are unique: one emphasizes alignment and long-term coverage, the other emphasizes speed and instant feedback. Both make testing less tedious if used in the right context.
When to Use JetBrains AI Assistant
Refactoring, renaming, and expanding features in large codebases work best with JetBrains AI. It uses project context to keep suggestions in line with the existing structure.
Unit tests and documentation can be generated right in the IDE, and cross-language conversions make multi-language projects easier.
Pros
- Keeps changes consistent across files and modules.
- Works naturally in JetBrains IDEs.
- Generates tests, documentation, and commit messages in context.
- Supports multi-language projects.
Cons
- Not as fast for one-off lines or small experiments.
- Heavier reliance on JetBrains IDEs.
- Some features need cloud access or subscription.
When to Use GitHub Copilot
Rapid prototyping, boilerplate, and quick inline suggestions fit Copilot’s strengths. It spits out code or test stubs quickly, which helps when exploring new modules or APIs.
It’s useful for experimenting or getting small pieces done without thinking too much about structure.
Pros
- Fast inline suggestions and multiple predictions.
- Works across many editors and IDEs.
- Great for experiments, prototyping, and small tasks.
Cons
- Larger or multi-file changes may need review.
- Logic alignment isn’t automatic.
- Cloud connection and subscription required for full features.
FAQs
Do JetBrains AI and Github Copilot replace code review?
No. They assist with coding and tests, but human oversight is still needed for logic and design choices.
How do subscriptions differ?
JetBrains AI has Free, Pro, Ultimate, and Enterprise plans with cloud credit quotas and local model support. Copilot has free and paid individual or organizational tiers, with advanced requests billed extra.
Are tests reliable?
The ones with JetBrains AI are- they’re structured and aligned with project patterns. Copilot is better suited for a rough project; tests are quick and experimental, frequently needing small adjustments.
Which tool suits junior developers?
JetBrains AI guides edits and patterns gently, so it’s the safest option for developers just starting out – though it’s good to have Copilot on hand for extra suggestions.
Can I work offline?
JetBrains AI supports local models and offline work. Github Copilot, though, needs cloud access for full functionality.
Final Thoughts And Recommendations
JetBrains AI works in the background to keep your project coherent, while Copilot gives live support. They both essentially cover each other’s weaknesses. So, you should have both at hand, and know when to use each. You can use them together to make your work feel less exhausting – and always come up with near flawless, with (mostly) no spaghetti code in the mix.
