pirol vs Referee.chat

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

pirol compared with Referee.chat
Detail	pirol	Referee.chat
What it does	AI lab researching human-agentic interaction and building tools for people and agents to work together	An AI panel debates your goal; a referee rules on re-checkable evidence
Stage	Not given	Not given
Founded	Not given	Not given
Based in	DE	Not given
Pricing model	Freemium	Freemium
Pricing	Pirol AI: Buy budget starting from €20 with no subscription or expiration; priced per model from €0.62/1M output tokens for Pirol AI's blended rate. GLM-5.3-Flash: €0.19 input, €0.62 output per 1M tokens. DeepSeek V4.1 Flash: €0.40 input, €1.59 output per 1M tokens. GLM-5.3: €1.47 input, €5.45 outpu	Free: $1.00 starting credit once. Credit plans from $10 top-up with no expiration. Your own key: 5% infrastructure fee after the first $5.00 of usage.
Key features	Desktop app for local AI agents Automatic CRM, ERP, HR software creation Available for macOS and Windows EU-hosted models with OpenAI-compatible API Company as Code markdown standard Model selection and routing Multiple model options including vision No subscription required Budget does not expire	Panel of up to six AI models from different vendors Referee rules on re-checkable evidence only Searches academic databases: arXiv, OpenAlex, Crossref, Europe PMC Executes Python code to verify claims Customizable acceptance standards Matches pause and resume without losing work Shared ledger prevents re-derivation Spending limit per match
What makes it different	All processing stays on your machine with no cloud uploads required Agents can be trusted with sensitive work because they are predictable and observable European hosting and data sovereignty Open-source Company as Code standard for describing organizations Model router picks best model per task	Referee decides on evidence, not argument quality Panel from different vendors fails in different directions Stuck is a targeted question, not model thrashing Cost grows with ledger, not round count Published matches become research record
Community votes	0	0
DR (Domain Rating by Ahrefs)	Not checked yet no change since last check	2 no change since last check
Trust Flow (Majestic)	0	0
Citation Flow (Majestic)	25	13
Referring domains (Majestic)	34	31

5 details filled in for both companies.

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