What is Marketing Model Arena?
Marketing Model Arena (MMA) is a platform that helps you find the best AI model for your marketing tasks. We compare real outputs from different AI models across various marketing scenarios, so you can see which models deliver the results you need for your campaigns.
How it works
Our platform uses real-world marketing test cases from leading brands. Each test case represents a common marketing challenge, and we generate outputs from multiple AI models for comparison.
- Compare side-by-side outputs from different AI models
- Vote on which outputs work best for your marketing needs
- View rankings based on community votes to discover top-performing models
- Explore model performance across different brands and industries
Why we built it
At pagent.ai, we work with marketing teams every day to help them leverage AI for their content creation. We noticed that choosing the right AI model for marketing tasks was becoming increasingly difficult as new models are released constantly.
Marketing Model Arena was built to solve this problem. Instead of relying on generic benchmarks or marketing claims, we provide a transparent platform where you can see real outputs and make informed decisions based on what actually works for marketing content. By crowdsourcing votes from marketing professionals, we create rankings that reflect real-world performance for marketing use cases.
Get started
Ready to find the best AI model for your marketing tasks? Start by voting on comparisons to see which models perform best, or explore the leaderboard to see current rankings.
ELO Rating System
Marketing Model Arena uses the ELO rating system to rank AI models based on community votes. The ELO system, originally developed for chess, is a proven method for ranking competitors based on pairwise comparisons. Here's how it works:
Initial Ratings
All models start with a default rating of 1500 points. This provides a neutral starting point that allows models to move up or down based on their performance in comparisons.
Expected Score Calculation
Before each comparison, the system calculates the expected probability that each model will win based on their current ratings. The formula used is:
This means a model with a higher rating is expected to win more often, but upsets are still possible and will result in larger rating changes.
Rating Updates
After each vote, ratings are updated using the formula:
The K-factor determines how much ratings can change from a single comparison:
- K = 24 for win/loss outcomes (standard comparisons)
- K = 16 for ties (when both outputs are equally good)
- K = 12 for "both bad" outcomes (when both outputs are unsatisfactory)
Outcome Types
The system supports four types of comparison outcomes:
- Left Better: The left model wins (scores 1 point, right scores 0)
- Right Better: The right model wins (scores 1 point, left scores 0)
- Tie: Both models perform equally well (both score 0.5 points)
- Both Bad: Neither model meets expectations (both score 0 points, causing ratings to decrease)
Segmented Ratings
Models receive separate ratings for different contexts, including language, text type, industry, and whether brand guidelines are provided. This ensures that rankings reflect performance in specific use cases rather than a single overall score.
User Voting
Community members vote directly on model outputs, comparing them side-by-side. The ELO rating system computes ratings directly from these user votes, allowing the community to shape the rankings based on real-world preferences and marketing expertise.
Marketing Model Arena is built by pagent.ai, a company dedicated to helping marketing teams create better content with AI. We build tools and platforms that make AI accessible and effective for marketing professionals.
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