Compare tools
Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
⇄ Comparison dimension — pick the market you're actually shopping in
✕
Code Arena
✓ verifiedFree
Side-by-side arena to compare AI coding models and build multi-file apps, with a public leaderboard and battle mode.
35M visits/mo201 saves
✕
Windsurf Editor
✓ verifiedFree trial
AI-powered code editor with agentic workflows for developers.
3.3M visits/mo
✕
Aider
✓ verifiedFree
Open-source terminal AI pair programmer that edits code in your local git repo and auto-commits, working with most LLMs.
479K visits/mo
✕
Supernova.io
✓ verifiedFreemium
Design-system platform that packages tokens, code components, and rules into scoped context for AI coding agents.
93K visits/mo
Pricing
No public pricing
No public pricing
No public pricing
Pro: $35/mo per full seat (up to 15 seats, billed monthly)
Core features
- ✦Head-to-head model comparison
- ✦Battle mode matchups
- ✦Public model leaderboard
- ✦Multi-file app generation
- ✦File uploads as input
- ✦AI-powered code completion and suggestions
- ✦Automated lint fixing
- ✦Cascade agent for advanced coding assistance
- ✦Integrated app building and deployment
- ✦MCP server support for custom tools
- ✦Terminal command integration
- ✦Memory of codebase structure and workflow
- ✦Terminal-based AI pair programming
- ✦Edits code in your local git repo
- ✦Automatic git commits with messages
- ✦Codebase mapping for large projects
- ✦Works with cloud and local LLMs
- ✦Voice-to-code, image/web context, lint and test
- ✦Scoped MCP context distribution to multiple AI coding tools
- ✦Design token and component API management
- ✦Collaborative documentation with analytics
- ✦Figma and Storybook data source integration
- ✦Feedback loop for improving AI context quality
- ✦Skill and exporter management for agent capabilities
Use cases
- →Choosing the best coding model
- →Benchmarking AI code quality
- →Prototyping small apps
- →Accelerating software development by automating repetitive tasks
- →Reducing onboarding time for new developers
- →Improving code quality and reducing tech debt
- →Streamlining the app building and deployment process
- →Enhancing developer productivity by keeping them in a state of flow
- →Building features and fixing bugs via AI in the terminal
- →Working on large existing codebases
- →Automating git commits
- →Using local LLMs for private coding
- →Product teams giving AI coding agents accurate design-system context
- →Design system managers publishing a single source of truth
- →Engineering teams reducing token usage by scoping agent context per team
Visit