Comparing Large Language Models: Everest Group’s AI LLM Assessment Provides a Powerful Framework for Enterprise Decision-Making | Blog

Selecting the right Large Language Model (LLM) is critical for developing the best-suited generative AI (gen AI) solution. However, choosing an LLM on the number of parameters alone can be a costly mistake as larger size alone doesn’t always equate to better performance. Discover a comprehensive framework that evaluates and compares more than a dozen LLMs on 27 key parameters to enhance enterprise decision-making.  

Since the fervor surrounding gen AI started in November 2022, the explosion of LLMs is redefining language understanding and generation boundaries. As more models continue to emerge, evaluating them presents a significant challenge. A more structured and detailed approach is critically needed to evaluate these massive models that goes beyond assessing them solely on their sheer number of parameters.

Given the rising interest in gen AI across diverse applications, the lack of comprehensive research into LLM evaluation is striking. Relying solely on the parameter count when choosing an LLM can be misleading. It neglects crucial performance aspects, increases implementation costs, hinders enterprise readiness, enhances risk, and more. As these LLMs shape interactions and decision-making, an all-inclusive evaluation framework is essential to navigate their impact effectively.

Building on inaugural research in this area, Everest Group has assessed LLMs on multiple parameters and showcased how they rank against each other to help enterprises make the best and most informed decisions.

Introducing Everest Group’s AI LLM Assessment

Everest Group’s AI LLM Assessment presents a comprehensive framework, offering valuable guidance for stakeholders seeking to understand the various elements of LLMs. This assessment meticulously evaluates 13 leading LLMs across 27 distinct dimensions.

The framework evaluates LLMs’ unique capabilities, enabling a deeper understanding of their functionalities. Consequently, enterprises can determine which LLMs are fast, user-friendly, and capable of handling large amounts of input data for practical implementations.

The AI LLM Assessment evaluates various capabilities through such dimensions as the number of tokens they can process, the modalities supported, inference speed, training data quality, and overall market perception. These factors ultimately become differentiators, setting LLMs apart from peers and predecessors.

Below is a snapshot of the Everest Group AI LLM assessment matrix or explore the full framework.


The hype of large language models – is bigger always better?

Recently, we have witnessed numerous technology providers developing their LLMs. Each model aims to outperform the others by emphasizing its larger size compared to its peers and previous iterations. However, it is rarely discussed whether having more parameters and larger datasets actually enhances the ability to deliver value across various use cases.

LLM advancements have highlighted a fascinating trend where smaller models like PaLM 2 have demonstrated superior performance despite being trained on fewer parameters than their predecessors. These compact models not only offer better performance but also deliver faster inference times and reduced processing costs. This underscores that larger models may not always be the only way to achieve the desired outcomes.

Choosing the right LLM

Deciding which LLM is the best fit for enterprise applications and use cases based on LLM capabilities and features is the most crucial step in developing a gen AI solution. After assessing a model for these variables, understanding how easily it can be integrated into enterprise operations is vitally important.

To address this need, the framework takes into account the feasibility of practical implementation, considering factors such as the average implementation cost based on usage and ecosystem readiness. The framework also examines the selected LLMs for potential risks that may hinder enterprise adoption.

By considering capability versus adoption ease, the framework offers enterprises a balanced approach for analyzing LLM attributes and functionalities while also accounting for the associated challenges and considerations to integration and utilization.

The path to enhanced LLM performance and adoption

This framework has the potential to help developers enhance their capabilities over peers in building new LLMs tailored for specific tasks or applications by better understanding competitors’ strengths and weaknesses.

While LLM evaluation is undeniably complex and continuously evolving, this framework provides a vital starting point. As Everest Group continues to track developments in the gen AI landscape, we welcome discussing potential use cases, risk and cost considerations, and the impact of gen AI across various industries.

Please reach out to Priya Bhalla, [email protected], Vishal Gupta, [email protected], Vaibhav Bansal, [email protected], Yukta Sharma, [email protected], or Vatsalya Singhal, [email protected] to discuss generative AI topics further.

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