Claude vs Gemini 3 vs GPT-4 in AI IDEs: Which Model Powers Your Development Best? | Antigravity AI Directory | Google Antigravity Directory
Claude vs Gemini 3 vs GPT-4 in AI IDEs: Which Mode... Best Practices Claude vs Gemini 3 vs GPT-4 in AI IDEs: Which Model Powers Your Development Best? Claude vs Gemini 3 vs GPT-4 in AI IDEs: Which Model Powers Your Development Best?
The AI IDE landscape offers different model choices. Google Antigravity uses Gemini 3, Cursor uses Claude, and GitHub Copilot uses GPT-4. This guide compares these models for development tasks.
Model Overview
Gemini 3 (Google Antigravity)
Developer: Google DeepMindContext window: Up to 1M tokens (Ultra)Strengths: Multimodal, massive context, Google integrationRelease: 2024
Claude 3.5 Sonnet (Cursor)
Developer: AnthropicContext window: 200K tokensStrengths: Code quality, safety, reasoningRelease: 2024
GPT-4 Turbo (GitHub Copilot)
Developer: OpenAIContext window: 128K tokensStrengths: General knowledge, API ecosystemRelease: 2023-2024
Head-to-Head Comparison
Code Generation Quality
Aspect Gemini 3 Claude 3.5 GPT-4 TypeScript ★★★★★ ★★★★★ ★★★★☆ Python ★★★★★ ★★★★★ ★★★★★ React/Next.js ★★★★★ ★★★★★ ★★★★☆ System design ★★★★☆ ★★★★★ ★★★★☆ Edge cases ★★★★☆ ★★★★★ ★★★★☆
Speed and Latency
Inline completion ~200ms ~300ms ~250ms Full function ~2s ~3s ~2.5s Large refactor ~10s ~15s ~12s
Context Handling Gemini 3 Ultra: 1,000,000 tokens
Claude 3.5 Sonnet: 200,000 tokens
GPT-4 Turbo: 128,000 tokens
Winner for large codebases: Gemini 3
Real-World Test Cases
Test 1: React Component Generation Prompt: "Create a data table component with sorting, filtering, pagination, and row selection"
// Complete implementation with all features
// Includes TypeScript types
// Uses modern React patterns
// 200 lines, well-structured
// Score: 9/10
// Complete implementation with accessibility
// Exceptional error handling
// Includes keyboard navigation
// 250 lines, very thorough
// Score: 9.5/10
// Functional implementation
// Basic TypeScript types
// Standard patterns
// 180 lines, works correctly
// Score: 8/10
Test 2: Bug Fix Analysis Prompt: "Find and fix the bug in this authentication code"
Gemini 3: Identified race condition, provided fix with explanation. Suggested additional improvements.
Claude 3.5: Found the bug plus two potential security issues not mentioned. Comprehensive fix with test cases.
GPT-4: Found the primary bug, provided working fix. Less detailed explanation.
Test 3: Architecture Recommendations Prompt: "Design a scalable real-time notification system"
Gemini 3: Provided Google Cloud-centric solution with Pub/Sub, excellent diagrams, comprehensive trade-offs.
Claude 3.5: Cloud-agnostic design with multiple options, deep reasoning about scaling, security considerations.
GPT-4: Solid design with AWS focus, good explanations, practical implementation steps.
Strengths by Use Case
Best for Web Development: Tie (Gemini 3, Claude 3.5)
Modern React/Next.js
TypeScript
CSS/Tailwind
API development
Best for Large Codebases: Gemini 3
1M token context
Understands entire projects
Cross-file reasoning
Best for Code Quality: Claude 3.5
Exceptional edge case handling
Security awareness
Thorough testing suggestions
Best documentation
Best for Quick Completions: Gemini 3 Flash
Fastest response times
Good for inline suggestions
Lower latency
Best for Legacy Code: Claude 3.5
Pattern recognition
Refactoring suggestions
Maintains compatibility
IDE Integration Quality
Google Antigravity (Gemini 3)
Native integration
MCP server support
Browser agent
Google ecosystem (Cloud, Firebase)
Newer, less mature
Google account required
Pricing can be unclear
Cursor (Claude 3.5)
Mature product
Excellent UI
Strong community
Multiple model options
Separate subscription
Context limits
GitHub Copilot (GPT-4)
GitHub integration
Enterprise features
Wide language support
Established ecosystem
Less agentic
Smaller context
Less code-focused
Pricing Comparison Product Free Tier Pro Price Best Value Antigravity 50 req/day $20/mo Large projects Cursor 200 req/mo $20/mo Code quality Copilot None $10/mo GitHub users
Switching Models
When to Use Gemini 3
Large monorepo projects
Need massive context
Google Cloud stack
Multimodal needs (images, docs)
When to Use Claude
Security-critical code
Complex refactoring
Documentation-heavy work
Need thorough explanations
When to Use GPT-4
Already in GitHub ecosystem
Team standardization
General-purpose needs
Enterprise requirements
Future Outlook
Gemini 3
Continuous improvement from Google
Integration with more Google services
Potentially larger context windows
Better agentic capabilities
Claude
Focus on safety and reliability
Computer use capabilities
Enterprise features
API improvements
GPT-4/GPT-5
Multi-modal expansion
Reasoning improvements
Enterprise security
API ecosystem growth
Recommendation Matrix If you... Choose... Have a large codebase Gemini 3 Prioritize code quality Claude 3.5 Use GitHub heavily GPT-4 Need fastest completions Gemini 3 Flash Work on security-critical apps Claude 3.5 Want Google ecosystem Gemini 3 Need best documentation Claude 3.5
Conclusion Each model has distinct advantages:
Gemini 3: Best for scale, speed, Google integrationClaude 3.5: Best for quality, safety, thorough analysisGPT-4: Best for ecosystem, enterprise, general use The best choice depends on your specific needs. Many developers use multiple tools for different tasks.
No single model is universally best
Context window matters for large projects
Code quality varies by task type
Integration quality affects productivity
Consider your existing ecosystem
Choose the right tool for your specific development needs.