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Referee.chat vs Letronna®

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

Referee.chat compared with Letronna®
DetailReferee.chat logo Referee.chatLetronna® logo Letronna®
What it doesAn AI panel debates your goal; a referee rules on re-checkable evidence An AI agent with its own cloud and wallet
StageNot given Not given
FoundedNot given Not given
Based inNot given US
Pricing modelFreemium Not given
PricingFree: $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. Free: $0 forever with your own API keys. Pro: $20 per month with $20 of hosted credit a month (or $240 at once on yearly, save up to 17%). Team: $18 per seat, monthly, 2 to 5 people, with $18 of shared credit per seat monthly. Compute costs about $6 per GB a month from credit, charged by the second.
Key features
  • 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
  • Real files, real commands, web access, and browser beside the chat
  • Memory written to disk as plain text you can edit and delete
  • Auto mode with safe judgment on your own model
  • Delegate work to helper agents sharing a task list
  • Chat apps: Telegram, Discord and Slack integration
  • Support for MCP servers, plugins and IDE integration
  • Cloud tasks that keep running after you close the app
  • Services that stay online with scheduler, webhook and watcher
  • Web apps with their own live link at letronna.si
What makes it different
  • 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
  • Runs locally with your own keys; nothing leaves your machine unless you connect a hosted model
  • Starts in 7.65 ms with 84% on Harness-Bench
  • Plain text memory files you can edit, not a black box
  • Works with any model: OpenAI-compatible, Anthropic, Ollama, llama.cpp or hosted
  • Pay only for hosted models and cloud compute, not per seat
Community votes00
DR (Domain Rating by Ahrefs)2 no change since last check18 no change since last check
Trust Flow (Majestic)00
Citation Flow (Majestic)130
Referring domains (Majestic)310

4 details filled in for both companies.

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