Cursor vs JetBrains AI Assistant

Detailed side-by-side comparison to help you choose the right tool

Cursor

🔴Developer

AI code editor

Cursor is a ai code editor focused on daily software development, large-codebase navigation.

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Starting Price

Custom

JetBrains AI Assistant

AI Coding Tools

AI-powered plugin for JetBrains IDEs that enhances development workflow with intelligent code completion, next edit suggestions, AI chat with project context, and coding assistance inside JetBrains IDEs.

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Starting Price

Custom

Feature Comparison

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FeatureCursorJetBrains AI Assistant
CategoryAI code editorAI Coding Tools
Pricing Plans192 tiers4 tiers
Starting Price
Key Features
  • AI code editor with agent requests and Tab completions
  • Cloud agents plus terminal, Slack, and GitHub workflows
  • MCPs, skills, hooks, and frontier model access on paid plans
  • Mellum Local Code Completion
  • Junie Agent Mode
  • Next Edit Suggestions

💡 Our Take

Choose JetBrains AI Assistant if your team wants to keep IntelliJ IDEA, PyCharm, WebStorm, GoLand, Rider, or other JetBrains IDEs as the primary development environment. Choose Cursor if you are willing to adopt a standalone AI-first editor and want a workflow designed around multi-file AI composition rather than a plugin inside an existing IDE.

Cursor - Pros & Cons

Pros

  • Combines autocomplete, chat, and agent workflows in one polished editor
  • Strong fit for developers who want AI features always available, not bolted on
  • Codebase awareness is more useful than generic chat for existing repositories
  • MCP support gives a path to connect docs, tools, or internal services

Cons

  • Pricing could not be verified by curl during this run; confirm current Pro, team, and usage limits before purchase
  • Editor migration can be a blocker for teams standardized on another IDE
  • Agent edits still require review; generated code can introduce subtle architecture or security issues
  • Heavy AI use may create cost and governance questions for larger engineering teams

JetBrains AI Assistant - Pros & Cons

Pros

  • Deep native integration with JetBrains IDEs lets developers use AI assistance inside IntelliJ IDEA, PyCharm, WebStorm, GoLand, Rider, CLion, PhpStorm, RubyMine, DataGrip, DataSpell, Aqua, and related products instead of switching to a separate AI editor.
  • The 2026.1 IntelliJ IDEA Help page lists concrete workflow features: code completion, next edit suggestions, AI Chat, agent mode, context management, response processing, cloud LLM support, and local model use.
  • AI Chat can use explicit project context such as files, folders, images, symbols, commits, and other items, which is useful for questions tied to a real codebase rather than isolated snippets.
  • Response processing is practical inside the IDE because developers can review AI output and then apply code snippets, modify single or multiple files, or run terminal commands directly from AI Chat.
  • Pricing includes a no-cost AI Free tier with 3 AI Credits per 30 days and paid individual tiers starting at $10 USD per 30 days, which makes evaluation easier for existing JetBrains users before an organization rollout.
  • Supports both cloud-based providers named in the documentation, including Google Gemini, OpenAI, and Anthropic, and custom local third-party models for supported features such as code completion, in-editor code generation, or commit message generation.

Cons

  • JetBrains AI Assistant is not bundled or enabled by default; users must install the plugin, have a JetBrains AI Service license, and explicitly accept JetBrains AI terms before it can access code.
  • It is limited to the JetBrains IDE ecosystem, so teams using VS Code, Neovim, Sublime Text, or browser-based development environments will not get the same native plugin experience.
  • Cloud usage is quota-based: AI Free includes 3 AI Credits per 30 days, AI Pro includes 10 individual or 20 organization credits, and AI Ultimate includes 35 individual or 70 organization credits, so heavy agent or chat usage may require top-ups or a higher tier.
  • Advanced workflows such as agent mode, multi-file edits, terminal command execution, and generated tests still require careful developer review before code is merged.
  • Local model support exists, but setup and capability coverage are narrower than cloud use; the supplied data notes that MCP tool invocation is not currently supported when using local models.

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