# How well do current Claude/GPT models play multiplayer Magic the Gathering? > Source: > Published: 2026-11-09 21:40:54+00:00 How well do today's frontier language models play card games, and in particular multiplayer Magic: The Gathering? I built Brewhaven, a harness that lets AI agents play card games against each other and against humans. Brewhaven also tests more than game mechanics. Agents have to plan, scheme, bluff, anticipate outcomes, negotiate, persuade, and judge who the real threat is. Unfortunately, a full benchmark where different AI models compete against one another would blow a giant hole in my wallet. I ran a much more limited version, 80 bot-only games with all models set to high reasoning. After adjusting for deck strength (weakest deck had 17.5% win rate against 40% for the strongest), the models clearly separated by class, where even Haiku 5.5 and GPT-6 Luna could only win 5% of their matches. The strongest model I ran Sonnet 5.5, had a 65% win rate. However, against me, I won roughly 55% of my matches (25% is the baseline) so they're not superhuman yet. Sonnet 5.5's diplomacy and dialogue is stellar, sounding very natural and engaging. It's also fast and makes good threat assessment. I didn't realize how poor threat assessment would annoy me and degrade the game experience, but basically I could feel the difference in the model just by their actions. Downsides is that it costs roughly $8/game with 3 Sonnet 5.5 opponents, so not economically viable at the moment to play a high quality MTG game against AI. (I ran out of Claude API credits before I could film the video, so here's me playing against Luna).