Google Antigravity vs Kiro
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.
Kiro
Quick Verdict
Kiro excels at agentic workflows and spec-driven development with a score of 85/100. Kiro represents AWS's strategic entry into agentic coding, differentiated by its unique spec-driven development approach.
📊 Visual Score Comparison
Side-by-side comparison of key performance metrics across six evaluation criteria
Technical Specifications
| Feature | Google Antigravity | Kiro |
|---|---|---|
| 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. | Claude Sonnet 4.5 as primary model, with Auto mode that combines frontier models with prompt caching to optimize quality, latency, and cost. |
| Context Window | Supports large context windows through Gemini 3 Pro's advanced architecture, with multimodal processing of code, images, and design mocks. | Large context support through Claude Sonnet 4.5. Persistent context across sessions enables multi-day autonomous work without losing project understanding. |
| Deployment Options | Available as a downloadable desktop application for Windows, macOS, and Linux. Currently in public preview with enterprise features planned. | Standalone IDE (Code OSS-based) for macOS, Windows, Linux. CLI available for macOS and Linux. No AWS account required—sign in with GitHub, Google, AWS Builder ID, or IAM Identity Center. |
| Offline Mode | Limited offline capabilities; core agentic features require cloud connectivity for AI model inference and agent orchestration. | Cloud-based, requires internet connection. Core agentic features depend on cloud AI inference. |
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
Kiro Features
- Spec-driven development with auto-generated requirements.md, design.md, and tasks.md
- Autonomous agent that works for hours/days with persistent context
- Kiro Powers for dynamic context activation (Stripe, Figma, Datadog)
- Property-based testing (PBT) to verify code matches specifications
- Native MCP (Model Context Protocol) integration
- Agent hooks for automated documentation and testing on file events
- Multimodal chat supporting images and UI designs
- Agent steering files for project-specific customization
Pricing & Value Analysis
| Aspect | Google Antigravity | Kiro |
|---|---|---|
| Pricing URL | View Google Antigravity Pricing | View Kiro Pricing |
| Overall Score | 91/100 | 85/100 |
| Best For | Agentic Workflows, AI-Native Development, Multi-Agent Orchestration | Agentic Workflows, Spec-Driven Development, Enterprise Development, AWS Integration, Long-Running Tasks |
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
Kiro Excels At
- Converting product requirements into structured specs and implementation plans before writing any code—ensuring alignment between stakeholders and developers
- Running autonomous agents on complex features overnight, returning to completed implementations with full audit trails of decisions made
- Enterprise development where compliance requires traceable specifications that map directly to generated code artifacts
Performance & Integration
| Category | Google Antigravity | Kiro | Winner |
|---|---|---|---|
| IDE Support | Google Antigravity is a standalone AI-native IDE. Integrates with Google Cloud services and supports extensions. | Kiro is a standalone IDE based on Code OSS. Supports VS Code settings import, Open VSX extensions, and existing themes. CLI available for terminal workflows. | Tie |
| Community | Active community | Active community | Tie |
| Data Richness | Comprehensive | Comprehensive | Tie |
| Overall Score | 91/100 | 85/100 | Google Antigravity |
The Bottom Line
Both Google Antigravity and Kiro are capable AI coding tools, but they serve different needs. Google Antigravity scores higher (91/100 vs 85/100) 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 Kiro if: you prioritize agentic workflows and spec-driven development and don't mind a slightly lower score for specialized features.