Google Antigravity vs Sourcegraph Cody
Google Antigravity
Quick Verdict
Google Antigravity excels at agentic workflows and ai-native development with a score of 91/100. Google Antigravity represents a paradigm shift in AI-assisted development, moving beyond code completion to full agentic automation.
Sourcegraph Cody
Quick Verdict
Sourcegraph Cody excels at enterprise codebases and code search with a score of 89/100. Cody excels at understanding large, complex codebases across multiple repositories.
đ Visual Score Comparison
Side-by-side comparison of key performance metrics across six evaluation criteria
Technical Specifications
| Feature | Google Antigravity | Sourcegraph Cody |
|---|---|---|
| Core AI Model(s) | Powered by Google's Gemini 3 Pro model as default, with support for Anthropic's Claude Sonnet 4.5 and OpenAI's GPT-OSS for flexible model selection. | Not specified |
| Context Window | Supports large context windows through Gemini 3 Pro's advanced architecture, with multimodal processing of code, images, and design mocks. | Not specified |
| Deployment Options | Available as a downloadable desktop application for Windows, macOS, and Linux. Currently in public preview with enterprise features planned. | Not specified |
| Offline Mode | Limited offline capabilities; core agentic features require cloud connectivity for AI model inference and agent orchestration. | Not specified |
Core Features Comparison
Google Antigravity Features
- Agentic development with autonomous AI agents
- Dual interface: Editor View and Manager View
- Artifact transparency system for trust verification
- Multimodal capabilities (code, images, design mocks)
- Multi-model support (Gemini 3 Pro, Claude Sonnet 4.5, GPT-OSS)
- Self-improvement mechanism learning from user feedback
Sourcegraph Cody Features
- Deep codebase context and understanding
- AI-powered code search and navigation
- Multi-repository code intelligence
- Enterprise-grade security and deployment options
Pricing & Value Analysis
| Aspect | Google Antigravity | Sourcegraph Cody |
|---|---|---|
| Pricing URL | View Google Antigravity Pricing | View Sourcegraph Cody Pricing |
| Overall Score | 91/100 | 89/100 |
| Best For | Agentic Workflows, AI-Native Development, Multi-Agent Orchestration | Enterprise Codebases, Code Search, Large-scale Projects |
Best Use Cases
Google Antigravity Excels At
- Orchestrating multiple AI agents to work on different parts of a large codebase simultaneously
- End-to-end feature development from design mocks to implementation using multimodal AI
- Complex refactoring tasks with autonomous planning, execution, and validation by AI agents
Sourcegraph Cody Excels At
- Enterprise codebase navigation and understanding across multiple repositories and microservices
- Technical support and troubleshooting by analyzing complex codebases and providing contextual solutions
- Code modernization and migration projects with deep understanding of legacy system dependencies
Performance & Integration
| Category | Google Antigravity | Sourcegraph Cody | Winner |
|---|---|---|---|
| IDE Support | Google Antigravity is a standalone AI-native IDE. Integrates with Google Cloud services and supports extensions. | Multiple IDEs supported | Tie |
| Community | Active community | Limited community | Tie |
| Data Richness | Comprehensive | Basic | Google Antigravity |
| Overall Score | 91/100 | 89/100 | Google Antigravity |
The Bottom Line
Both Google Antigravity and Sourcegraph Cody are capable AI coding tools, but they serve different needs. Google Antigravity scores higher (91/100 vs 89/100) and excels in agentic workflows and ai-native development. The choice depends on your specific workflow, team size, and technical requirements.
Choose Google Antigravity if: you prioritize agentic workflows and ai-native development and want the higher-rated option (91/100).
Choose Sourcegraph Cody if: you prioritize enterprise codebases and code search and don't mind a slightly lower score for specialized features.