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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.

Gemini Code Assist logo
Gemini Code Assist
✓ verifiedFreemium

Google's AI coding assistant for code completion, generation, chat and review across IDEs and GitHub.

559K visits/mo
Warp AI logo
Warp AI
✓ verifiedFreemium

Agentic terminal and cloud agent platform (Warp Terminal, Warp Agent, Oz) for developers orchestrating Claude Code, Codex, and other agents.

1.7M visits/mo22K saves
CodeGPT logo
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 visits/mo
Workik logo
Workik
✓ verifiedFreemium

AI coding assistant that gathers project context to plan, generate, test and ship code across the SDLC via IDE and chat integrations.

210K visits/mo
Magic Patterns logo
Magic Patterns
✓ verifiedFreemium

AI prototyping tool that generates UI matching your design system, letting product teams test features fast.

242K visits/mo3.8K saves
Pricing

No public pricing

Free: $0/month (core terminal, limited cloud agent access)
Build: from $20/month pay-as-you-go (1,500 credits/month)
Max: from $200/month pay-as-you-go (12x Build credits)
Business: from $50/user/month (up to 25 seats)
Free: $0/mo (BYOK, 30 free interactions)
AutoComplete Add-on / BYOK Pro: $8/mo per seat ($6.67 annual)
Trial: $0/month (20 AI requests on signup, 7 requests/day, 50 free flow runs, up to 3 users)
Starter: $15/month billed annually or $25/month (20M standard AI tokens, 1,000 flow runs)
Premium: $30/month billed annually or $50/month (40M standard + 4M advanced AI tokens, 3,000 flow runs)
Elite: $80/month billed annually or $150/month (100M standard + 10M advanced AI tokens, 10,000 flow runs)
Tailored: $62/month (custom AI token allocation)

Free trial available

No public pricing

Core features
  • AI code completion and suggestions
  • Natural-language code generation
  • In-IDE chat assistance
  • AI code review
  • IDE integrations (VS Code, JetBrains, etc.)
  • GitHub integration
  • Modern terminal rebuilt for agentic coding workflows
  • Warp Agent with multi-agent orchestration and model routing
  • Oz platform for launching agents into the cloud via SDK, CLI, or terminal
  • Codebase indexing and granular permission controls
  • Team-wide usage visibility and spend/credit caps
  • Open-source terminal core
  • 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
  • Automatic context-gathering from connected engineering sources
  • AI-generated code, tests and pull requests from tickets
  • Task planning that breaks complex work into subtasks
  • Auto-updating engineering documentation
  • Vector search over embedded project data
  • Multiple selectable AI models (GPT, Gemini, Claude, Llama, etc.)
  • Engineering productivity analytics dashboard
  • AI UI generation from prompts
  • Match existing styling and design systems
  • Rapid, high-fidelity prototyping
  • Live team editing and sharing
  • Enterprise security and compliance
Use cases
  • Speeding up coding with AI completions
  • Generating code from plain-language prompts
  • Getting in-editor help and explanations
  • Reviewing pull requests with AI
  • Understanding unfamiliar codebases
  • Developers who want an AI-assisted terminal for daily coding
  • Teams orchestrating multiple coding agents (Claude Code, Codex) together
  • Engineering orgs needing governance over agent-driven development
  • Companies moving agent workflows from local machines to the cloud
  • Code generation, refactoring and debugging
  • Control AI spend with your own keys
  • Switch between frontier models per task
  • Keep code private and data-sovereign
  • Engineering teams automating ticket-to-PR workflows
  • Developers wanting AI-assisted debugging and test generation
  • Engineering managers tracking AI-driven productivity gains
  • Teams centralizing documentation from scattered sources
  • Prototype new product features
  • Test designs with customers
  • Build design-system-consistent mockups
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