Guide to the Five Most Powerful Large Language Models and How to Choose Them

Large language models (LLMs) are the core of generative AI, and some models are better suited for specific tasks; this guide lists the five most powerful models and how to use them.

Modern LLMs are pretrained on massive self‑supervised text corpora and then fine‑tuned with techniques such as reinforcement learning from human feedback (RLHF).

Over roughly the past decade LLMs have advanced rapidly, especially since the 2012 introduction of the Generative Pre‑trained Transformer (GPT). Notable milestones include Google’s BERT (2018), OpenAI’s GPT‑3 (2022), and the current GPT‑4.

While open‑source models raise concerns about misuse, recent alternatives like Meta’s Llama 2 demonstrate that viable open models are emerging.

Uses of LLMs

The capabilities of LLMs are impressive and span many business applications, such as chatbots for customer support, code generation for developers, audio transcription, summarization, rewriting, translation, and content creation.

For example, a real‑time LLM could transcribe and summarize a client meeting, with the results shared with sales, marketing, and product teams, or automatically translate a website into multiple languages, with human reviewers polishing the output.

In development environments, AI‑assisted code completion tools like GitHub Copilot and Amazon CodeWhisperer are already common, and LLMs can also power natural‑language database queries and generate developer documentation.

LLMs are also applied to sentiment analysis, helping organizations gather feedback, identify common themes, and inform data‑driven business strategies.

However, LLMs are not fully reliable, so any high‑accuracy scenario should involve human oversight.

Training an LLM from scratch remains a massive engineering effort; therefore, leveraging existing models is usually more practical. Based on expert input, we have identified five LLMs that are currently the most important to consider.

Top Five LLMs

GPT‑4

GPT‑4 is widely regarded as the best model today, backed by a robust ecosystem that supports plugins, code execution, and function calling, and it excels at text generation and summarization.

Claude 2

Anthropic’s Claude 2, released in July, is accessible via API and a public demo site (claude.ai). Its main advantage is a large context window—recently expanded from 9 K to 100 K tokens—far exceeding GPT‑4’s 32 K‑token limit, allowing it to ingest hundreds of pages of material.

Llama 2

Meta’s Llama 2 is the first open‑source model on our list. It can be used freely for research and commercial purposes, though the license imposes restrictions such as requiring a special Meta license for applications with over 700 million monthly users and prohibiting its use to train other language models.

While open‑source offers research benefits, the high cost of training and fine‑tuning means commercial LLMs often perform better.

Llama 2’s predecessor, LLaMA, was released under a non‑commercial license in February, quickly leaked, and spawned fine‑tuned variants like Stanford’s Alpaca and the Vicuna models from UC Berkeley, CMU, Stanford, and UC San Diego.

Because it is free, Llama 2 can be a good starting point for evaluating a specific use case.

Orca

Microsoft Research’s Orca is the most experimental model we selected; it is a smaller open‑source model that uses a technique called progressive learning to self‑train from larger base models.

This approach lets Orca imitate the reasoning abilities of larger models such as GPT‑4, suggesting a potential path for open‑source models to compete with closed‑source counterparts.

Cohere

Cohere, founded by Aidan Gomez (co‑author of the seminal “Attention Is All You Need” paper), offers a cloud‑agnostic commercial LLM aimed at enterprise customers, as evidenced by its recent partnership with McKinsey.

How to Choose an LLM

After compiling a shortlist of candidate LLMs and identifying one or two low‑risk use cases for experimentation, run multiple tests with different models to determine which best fits your needs, similar to evaluating observability tools.

Consider using multiple LLMs simultaneously; future workflows may involve a suite of models each excelling at different tasks.

Access to timely data is essential—without it, the models cannot deliver value. Context databases like SingleStore can help bridge this gap.

To fully leverage LLM capabilities, you need lexical and semantic search, handling of structured and unstructured data, metadata and vectorized data processing, all at millisecond latency between the end user and the LLM response.