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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.

CodeGPT
✓ verifiedFreemium

IDE coding assistant for VS Code and JetBrains that uses your own API keys across 15+ model providers, with agentic mode and autocomplete.

👁 262K/mo

Thin 'Lingbot-map' agent listing on github.com with zero traffic; too thin to tell.

5.2K
Apify
✓ verifiedFreemium

Full-stack platform for web scraping, data extraction, and automation; category leader.

👁 4.4M/mo2.0K
Code Autopilot
✓ verifiedFreemium

AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.

Pixels2Flutter
✓ verifiedFree

Turns UI screenshots into working Flutter code.

12K
Pricing
Free: $0/mo (BYOK, 30 free interactions)
AutoComplete Add-on / BYOK Pro: $8/mo per seat ($6.67 annual)

No public pricing

Free: $0/mo ($5 included usage)
Starter: $29/mo ($26/mo billed annually)
Scale: $199/mo ($179/mo billed annually)
Business: $999/mo ($899/mo billed annually)

Free trial available

No public pricing

No public pricing

Core features
  • BYOK access to 15+ model providers
  • Agentic planning-then-build mode
  • AI autocomplete
  • MCP connections to external systems
  • Custom rules and live context tracking
  • Local models via Ollama/LM Studio
  • VS Code and JetBrains plugins
  • Fast tensor operations
  • Differentiable tensors for gradient-based optimization
  • Network connectivity
  • Integration with Bun and Flashlight
  • Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
  • Web scraping
  • Data extraction
  • Browser automation
  • AI agents
  • Anti-blocking
  • Proxy rotation
  • Open-source tools (Crawlee)
  • Ready-made tools and code templates
  • Chat inside GitHub issues and PRs
  • Task-to-implementation plans with code
  • Automatic bug-fix suggestions
  • Pull-request summaries for faster review
  • Full-codebase context
  • GitHub-native integration
Use cases
  • Code generation, refactoring and debugging
  • Control AI spend with your own keys
  • Switch between frontier models per task
  • Keep code private and data-sovereign
  • Creating and manipulating datasets
  • Training small machine learning models
  • Implementing advanced training and inference logic
  • Building applications that require tensor computations
  • Data for generative AI
  • Lead generation
  • Market research
  • Sentiment analysis
  • Speeding up pull-request reviews
  • Implementing features from task descriptions
  • Debugging with AI-proposed solutions
  • Answering questions about a repo
  • Boosting a solo developer's output
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