Edge Arena vs Elpis
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Edge Arena compared with Elpis
| Detail | 
Edge Arena | 
Elpis |
|---|
| What it does | Put your business decisions on trial with competing AI agents
| Portable context for terminal coding agents
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| Stage | Not given
| Not given
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| Founded | Not given
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| Based in | Not given
| Not given
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| Pricing model | Freemium
| Open source
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| Pricing | Free: 2 free runs included with signup. Paid plans: Pro $12/month (1,344 credits, ~4 balanced runs/month), Power $29/month (3,360 credits, ~10 balanced runs/month), Studio $99/month (12,000 credits, ~35 balanced runs/month). One-time credit packs: Starter $9 (720 credits), Velocity $19 (1,680 credit
| Not given
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| Key features | - Multiple competing AI agents explore different strategic approaches simultaneously
- Adversarial critique phase eliminates weak plans before output
- Scoring rubric ranks finalist plans on consistent criteria
- Execution packs with structured next steps and implementation guidance
- Public or private run options with control over data sharing
- Prompts and results never used to train AI models
- View runner-up results and elimination reasoning
- Live real-time competition observation dashboard
- External developer agent deployment with rewards
| - Context Ledger for curating admitted instructions, files and memory
- Deterministic context admission before model processing
- Workflows with Mother agent team design and checking
- Love Spot drawing board with Matilda companion
- Desktop app with theme customization
- Support for GPT, Claude and Gemini models
- Terminal and desktop app sharing state
- Portable context for terminal coding agents
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| What makes it different | - Multi-agent competition vs single AI answer reduces blind spots through structured debate between opposing perspectives
- Adversarial critique phase pressure-tests assumptions and eliminates fragile plans before presentation
- Execution-ready structured output designed for immediate action vs theoretical analysis
- Data privacy protection with no model training on user prompts or results
| - Deterministic context admission ensures agents see processed context before model receives it
- Context Ledger provides transparent curation of agent inputs
- Mother agent orchestrates multi-agent workflows with checking and merged results
- Collaborative drawing interface with AI companion
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 28 ● no change since last check | 7 ● no change since last check |
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| Trust Flow (Majestic) | 9 | 0 |
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| Citation Flow (Majestic) | 32 | 12 |
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| Referring domains (Majestic) | 180 | 6 |
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4 details filled in for both companies.
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