Can Llama catch up with GPT on performance?

"Evaluating Llama and GPT: LLM Adoption in Enterprises" benchmarks large language models (LLMs). Specifically, it evaluates how Llama 3.1, Llama 3.2, GPT-4, and GPT-4o perform against each other. It discusses the key concerns around LLM adoption enterprises and in industries such as healthcare, legal, and finance, where they deal with a lot of sensitive data. You will have access to proprietary test and experiment results around how open-sourced Llama in self-hosted environments fared against GPT in tasks like summarization, reasoning, and such.

The research uses some of the most critical evaluation frameworks, such as DeepEval and LegalBench, and benchmarks such as MMLU, BIG-Bench Hard, and Text2SQL. We evaluated the performance of each LLM model against key metrics such as answer relevancy, faithfulness, hallucination, and toxicity. We provide comparative results to enumerate the strengths and weaknesses of each model.

These metric-driven insights and verified benchmarks will enable digital leaders and AI practitioners to make informed decisions about LLM deployment. It also highlights the potential of Llama models to address critical enterprise needs while maintaining control over proprietary data, bridging the gap between GenAI’s promise and its real-world application.