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Compare LLM's performance at scale with PromptFoo

I have previously discussed considering alternatives to OpenAI for productive, real-world applications. There are dozens of such alternatives, and many open-source options are particularly intriguing.

By comparing these solutions, you can potentially save money while also achieving better quality and speed.

For example, in a previous blog post I talked about the noticeable speed differences between models like OpenAI's gptg-4o - its fastest model - and LLAMA-3 on Together and Fireworks AI. They can deliver up to 3x the speed at a fraction of the cost.

How to compare LLMs 

There are various ways to compare models. You can use tools like openwebui if you’re doing it occasionally or if you have just a few models to test. There are also many commercial model comparison tools you could leverage.

In a business setting, where you have dozens of prompts and use cases across many models, you may want to automate this evaluation. Having a tool that supports such tasks can be very useful. One tool I frequently use is Promptfoo.

In this blog post, we will explore how to use Promptfoo to compare the performance and quality of responses from alternative models to OpenAI—in this case, a llama-based model from Together AI. Of course, you can adapt this approach to any other model or scenario you like.

Let’s get started.


1. Introduction to Promptfoo

Promptfoo is an open-source tool designed to help evaluate and compare large language models (LLMs). It enables developers to systematically test prompts across multiple LLM providers, evaluate outputs using various assertion types, and calculate metrics like accuracy, safety, and performance. Promptfoo is particularly helpful for those building business applications who need a straightforward, flexible, and extensible API for LLM evaluation.

Read more in my Blog

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