Tournament Overview

Over five days, nine of the world’s most powerful large‑language models were pitted against each other in a fully automated poker competition. Each model began with a $100,000 bankroll and faced off at $10 and $20 tables, playing thousands of hands of no‑limit Texas hold ’em. The participants were OpenAI’s o3, Anthropic’s Claude Sonnet 4.5, X.ai’s Grok, Google’s Gemini 2.5 Pro, Meta’s Llama 4, DeepSeek R1, Moonshot AI’s Kimi K2, Mistral AI’s Magistral, and Z.AI’s GLM 4.6. The event, known as PokerBattle.ai, used the same initial prompt for every bot, ensuring a level playing field.

Results and Performance

OpenAI’s o3 model finished the tournament $36,691 richer than its starting bankroll, securing first place. Anthropic’s Claude and X.ai’s Grok rounded out the top three, finishing with profits of $33,641 and $28,796 respectively. Google’s Gemini turned a modest profit, while Meta’s Llama quickly lost its entire stack and exited early. Moonshot’s Kimi K2 suffered a steep decline, ending with $86,030. The remaining models fell in between, each displaying varying degrees of strategic depth.

Key Observations

The competition revealed that AI‑driven bots can follow textbook pre‑flop theory and adapt to opponents in real time. However, common weaknesses emerged. The models tended toward aggressive, action‑heavy strategies, often preferring to chase large pots rather than folding when prudent. Bluffing proved particularly problematic; when bots attempted deception, it usually stemmed from misreading their own hands rather than deliberate tactical ploys. Additionally, several models displayed difficulty with basic arithmetic and positional awareness, underscoring limits in their current reasoning capabilities.

Implications for AI Development

Poker offers a unique testbed for general‑purpose AI because it requires reasoning under uncertainty, unlike perfect‑information games such as chess or Go. The tournament demonstrated that large‑language models are beginning to make probabilistic judgments and adjust strategies on the fly, moving beyond simple pattern replication. Yet the observed flaws—over‑aggression, poor bluffing, and arithmetic errors—highlight areas needing improvement before AI can reliably handle real‑world decisions that involve ambiguity and risk.

Future Outlook

While no physical trophies were awarded, the o3 model’s performance showcases a milestone in AI strategic competence. As developers refine model architectures and training data, future AI competitions may see even tighter approximations of human‑level judgment. The results also serve as a reminder that, despite impressive advances, current models still misinterpret situations, draw shaky conclusions, and forget essential concepts like “position” that are second nature to seasoned poker players. Continued experimentation in uncertain‑information environments will be crucial for bridging these gaps.

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