Google Antigravity vs Kiro
Google Antigravity is best for Agentic Workflows, while Kiro targets Spec-Driven Development. On our independent 100-point evaluation, Google Antigravity scores 92/100 vs Kiro's 82/100 — a 10-point gap reflecting measurable differences across ten capability dimensions.
Google Antigravity
Quick Verdict
Google Antigravity focuses on Agentic Workflows and AI-Native Development and scores 92/100 in our independent evaluation. Google Antigravity represents a paradigm shift in AI-assisted development, moving beyond code completion to full agentic automation.
Kiro
Quick Verdict
Kiro focuses on Agentic Workflows and Spec-Driven Development and scores 82/100 in our independent evaluation. Kiro represents AWS's strategic entry into agentic coding, now encompassing what was previously Amazon Q Developer CLI.
📊 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 |
|---|---|---|
| Overall Score | 92/100 | 82/100 |
| Best For | Agentic Workflows, AI-Native Development, Multi-Agent Orchestration | Agentic Workflows, Spec-Driven Development, Enterprise Development, AWS Integration, Long-Running Tasks |
| Detailed Pricing | View Google Antigravity pricing | View Kiro pricing |
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 |
|---|---|---|---|
| Overall Score | 92/100 | 82/100 | Google Antigravity |
| IDE Support | Google Antigravity is a standalone AI-native IDE. Integrates with Google Cloud services and supports… | Kiro is a standalone IDE based on Code OSS. Supports VS Code settings import, Open VSX extensions, a… | Tie |
| Founded | NaN | NaN | Tie |
| Community Channels | 2 channels | 3 channels | Kiro |
Google Antigravity vs Kiro: Data-Driven Comparison
This section is auto-generated from the underlying data in Google Antigravity's and Kiro's published specifications — no marketing copy. Each row below contrasts a specific capability area using the fields we track in our scoring methodology.
Underlying AI models
Google Antigravity: 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 se… Kiro: 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 handling
Google Antigravity: Supports large context windows through Gemini 3 Pro's advanced architecture, with multimodal processing of code, images, and design mocks. Kiro: Large context support through Claude Sonnet 4.5. Persistent context across sessions enables multi-day autonomous work without losing project…
Deployment & IDE footprint
Google Antigravity: Available as a downloadable desktop application for Windows, macOS, and Linux. Currently in public preview with enterprise features planned. Kiro: Standalone IDE (Code OSS-based) for macOS, Windows, Linux. CLI available for macOS and Linux. No AWS account required—sign in with GitHub, G…
Offline operation
Google Antigravity supports offline / local inference. Kiro requires an active internet connection.
Where each tool specializes
Google Antigravity targets AI-Native Development and Multi-Agent Orchestration. Kiro targets Spec-Driven Development and Enterprise Development. This divergence matters when matching a tool to a team's primary workflow.
Overall scoring gap
Google Antigravity scores 92/100 versus Kiro's 82/100 in our ten-dimension evaluation. This reflects measurable coverage differences; read each criterion in the Technical Specifications table above.
Choose Google Antigravity when Agentic Workflows maps directly to your main workflow and the data points above lean in its favor.
Choose Kiro when Agentic Workflows is the higher-priority capability for your team.
The Bottom Line
Google Antigravity and Kiro each serve different needs. Google Antigravity scores higher (92/100 vs 82/100) and tends to excel in Agentic Workflows and AI-Native Development. The right pick depends on your workflow, team size, and technical constraints.
Choose Google Antigravity if: you prioritize Agentic Workflows and AI-Native Development and want the higher-rated option (92/100 vs 82/100).
Choose Kiro if: you prioritize Agentic Workflows and Spec-Driven Development and accept a slightly lower headline score for its specialized fit.
Get the full comparison wallchart — scores, features, and decision guide in one printable PDF.
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