TL;DR:

  • JetBrains AI Assistant now supports multiple underlying models including Claude and GPT-4o, and its deep integration with the JetBrains IDE context (type system, project structure, run configurations) produces more accurate suggestions than context-agnostic assistants in complex Java, Kotlin, Python, and .NET codebases
  • The full code rewrite and multi-file edit features (available since late 2025) bring JetBrains closer to Cursor-style agentic editing, though it remains more conservative in scope than Cursor or Windsurf
  • For teams already on JetBrains IDEs with All Products Pack licenses, AI Assistant is included — making the build-vs-buy decision straightforward

JetBrains has been the home of serious Java, Kotlin, Python, and .NET development for over two decades. IntelliJ IDEA, PyCharm, Rider, and the rest of the JetBrains family are deeply integrated into enterprise software development workflows in ways that VS Code-based tools often don’t replicate well: rich type inference, refactoring tools that understand the full project graph, framework-specific support that goes well beyond syntax highlighting.

When JetBrains launched AI Assistant, the obvious question was whether it could match the AI capabilities of Copilot and Cursor while preserving what makes JetBrains IDEs valuable. In 2026, the answer is: largely yes, with some meaningful gaps still remaining.

What’s New in 2026

JetBrains AI Assistant has shipped significant updates over the past year:

Model flexibility: AI Assistant now lets you choose your underlying model. The options include JetBrains’ own models, Claude (via Anthropic’s API), GPT-4o, and Gemini Pro. You can switch models per-session or set a default per project type. Java and Kotlin users often find Claude handles complex refactoring instructions better; Python data science workflows tend to favour GPT-4o for pandas and NumPy-specific suggestions.

Full file and multi-file edits: Previously AI Assistant could suggest code within a single method or class. The updated version supports full-file rewrites and can edit across multiple files in a coordinated change — renaming a class and updating all its usages across the project, for example, or updating an API endpoint definition and the corresponding client code simultaneously.

Inline chat in the editor: The Copilot-style inline chat (select a block, ask a question, get a response in context) now works well in JetBrains. The key difference from Copilot is that the AI has access to the JetBrains semantic index — it knows the actual types, inheritance hierarchy, and usage patterns of the code you’re editing, not just the text.

Test generation: AI Assistant can generate unit tests from a function or class selection. For Java/JUnit and Python/pytest, the generated tests are substantially better than generic AI-generated tests because the assistant understands the testing framework’s annotations and idioms, not just the assertion syntax.

The JetBrains Advantage: IDE-Aware AI

The key differentiator for JetBrains AI Assistant compared to tools that add AI as a layer on top of VS Code is access to the JetBrains semantic model. When you ask AI Assistant to refactor a method, it doesn’t just read the method text — it can query the IDE’s index for:

  • All callers of the method and their argument types
  • The class hierarchy and interface contracts the method must satisfy
  • Existing test coverage for the code being changed
  • Framework-specific constraints (Spring beans, dependency injection, JPA entity rules)

This means refactoring suggestions are better scoped. When you ask it to change a method signature, it knows whether that change will break existing callers without having to guess from text analysis.

For large Java or Kotlin codebases with deep class hierarchies, this context awareness makes a real difference. Cursor’s global codebase indexing is impressive, but it’s text-based; JetBrains AI Assistant’s suggestions are informed by the actual semantic model the IDE maintains.

Limitations Worth Knowing

Agent mode is more conservative than Cursor: JetBrains AI Assistant’s multi-file editing works well for coordinated refactors, but it’s less willing to make sweeping changes autonomously than Cursor or Windsurf. It tends to ask for confirmation at more steps. This is arguably a feature for enterprise teams that want human review in the loop, but it’s slower for rapid prototyping.

Context window limitations: For very large codebases, the assistant hits limits on how much context it can include in a single prompt. JetBrains is addressing this with project-wide indexing that works alongside the context window rather than relying solely on it, but for monorepos with hundreds of modules, context selection is still imperfect.

No standalone web interface: JetBrains AI Assistant lives in the IDE. There’s no equivalent to Claude.ai or ChatGPT for ad-hoc queries outside the editor. If you want to draft documentation, plan architecture, or explore design questions outside the IDE, you need a separate tool.

Python data science tooling: PyCharm’s AI Assistant is good for Python development but less optimised for Jupyter notebook workflows than tools like GitHub Copilot with Jupyter support or purpose-built data science environments. Notebook cell completion and inline documentation work, but the experience is smoother in VS Code with Copilot for heavy notebook work.

Pricing and Who It’s For

JetBrains AI Assistant is included in the All Products Pack subscription (£65.90/month for individuals in 2026, with significant discounts for teams). For developers already paying for JetBrains All Products Pack, this is a zero-additional-cost upgrade.

Standalone AI Assistant is also available as a separate subscription for users of individual JetBrains products — useful if you only use IntelliJ IDEA or PyCharm and don’t need the full product pack.

It’s the right choice for: Java/Kotlin developers who live in IntelliJ IDEA, enterprise .NET teams on Rider, Python backend teams in PyCharm who want IDE-aware AI without switching editors, and any team where JetBrains tooling is already standardised.

It’s probably not the right choice for: Developers working in VS Code or Vim who don’t want to change editors, data scientists doing primarily Jupyter work, or teams whose projects are primarily frontend TypeScript where VS Code-native tools have a home-field advantage.

For JetBrains users, the 2026 question is no longer whether AI Assistant is mature enough to use daily — it is. The question is whether you want AI Assistant alongside your existing workflow or whether you want to move toward a more agent-first editor like Cursor for new projects. Many developers are using both.