Part 3/9:
While benchmark figures for proprietary models are often proprietary or optimized, community-driven leaderboards—like the LM Arena—offer more transparent insights. For example, Meta's 27-billion-parameter LLaMa model performs impressively on these scales, ranking well among many models despite its relative size. This demonstrates that, for many applications, open models can deliver high-quality results, especially when combined with techniques like few-shot prompting.
However, it's essential to approach these benchmarks with some skepticism, recognizing that models may be optimized or cherry-picked for specific tests. Nonetheless, the practical performance of these models in real-world tasks tends to align with their published results.