The above leaderboards represent how different generative AI models relatively stack up against each other. It's designed to give businesses and researchers a reference point, making identifying models that might fit their requirements easier. However, it cannot be a definitive indicator because the models listed on the leaderboards are ranked based on their capability to handle general tasks. For building domain-specific applications, for example, a health advisory app, a model fine-tuned with health-related data might be a better fit. In such situations, it's about more than just the size or generality of the model. Smaller, domain-focused models can sometimes outperform larger ones in terms of accuracy for specific tasks.
Each leaderboard employs a distinct methodology to assess the models featured on it. Generally, these models are evaluated based on their capability, the benchmark results provided in the models' research papers, the extent of community adoption, and the expenses associated with training them. For those interested in understanding the specific evaluation criteria for a model, the methodology is detailed on the respective leaderboard page.
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