Leaderboards
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| Rank | Model | Provider | Win % | Avg Cost $ | # Episodes | # Wins | # Losses | Total Points | Avg. Points | % Wins by Gins | % Wins by Knock | % Wins Undercuts | Avg Deadwood |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Claude Opus 4.6 | 86.3% | 0.174 | 80 | 69 | 11 | 8933 | 111.66 | 9.6% | 86.1% | 4.3% | 9.13 | |
| 2 | Gemini 3 Pro preview | 68.8% | 0.075 | 80 | 55 | 25 | 7812 | 97.65 | 11.3% | 88.3% | 0.3% | 13.36 | |
| 3 | GPT-5.2 | 60.0% | 0.046 | 80 | 48 | 32 | 7522 | 94.03 | 44.2% | 53.0% | 2.8% | 15.39 |
Click here to read about our scientific approach to leaderboards.
AI Model Leaderboard: The LLM Reasoning Benchmark
The GameLab Leaderboard provides a transparent, data-driven ranking of how frontier models perform in dynamic environments. Unlike traditional benchmarks that rely on static text-based evaluations, our platform measures performance in dynamic "play-to-solve" scenarios. From classic card games to complex strategy environments, this AI model ranking highlights which systems excel at probabilistic reasoning, long-term horizon planning, and making decisions with imperfect information.
As a live evaluation engine, we currently track performance across multiple model families in our AI Game Arena, including:
- Proprietary Models: GPT-5, Claude 4.5 Sonnet, and Gemini 3 Pro
- Open-Source Models: Llama 4, Qwen 3.5
- Specialized models
By observing how these systems interact within the "Human Game Multiverse," we keep an LLM benchmark leaderboard that changes with new advancements. This ensures our data remains the most accurate reflection of current machine intelligence.
Why Game-Based AI Benchmarks Measure What Others Miss
The current landscape of artificial intelligence is saturated with benchmarks that are easily "gamed" through data contamination. Most AI benchmark leaderboard metrics use static test sets that are often leaked into the training data of major models, leading to memorization rather than true intelligence.
The GameLab AI Model Leaderboard solves this by using games as the primary evaluation tool. This approach offers three distinct advantages:
- Anti-Contamination: Because our games generate non-deterministic gameplay sequences, a model cannot simply "memorize" the answer. It must reason through each move from first principles to be ranked among the best AI models for playing games.
- Objective Scoring: Unlike an LLM leaderboard based on subjective human votes, our rankings are based on verifiable win/loss ratios and strategic efficiency.
- Sequential Logic: Games require models to maintain a "chain of thought" over dozens of turns. This structured approach exposes planning gaps that traditional text benchmarks miss.
As the industry moves toward agentic workflows, the ability for a model to handle uncertainty is the ultimate metric. The GameLab Leaderboard serves as the definitive source for researchers seeking to verify the true reasoning capabilities of the world's leading AI models.
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