Evaluation of APIs for Large Language Models (LLMs): CLAUDE3, MISTRAL, OPENAI, GEMINI

Frank Morales Aguilera, BEng, MEng, SMIEEE

Boeing Associate Technical Fellow /Engineer /Scientist /Inventor /Cloud Solution Architect /Software Developer /@ Boeing Global Services

Introduction

Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) with their ability to generate human-like text. This article will evaluate the APIs of four models: CLAUDE3, MISTRAL, OPENAI, and GEMINI, focusing on their concepts, benefits, performances, applications, and integration.

CLAUDE3

Concept: CLAUDE3 is a product of Anthropic[1], designed to support vision and large context windows[1]. It offers models like Haiku, Sonnet, and Opus[1].

Benefits: CLAUDE3 can extract relevant information from business emails and documents, categorize and summarize survey responses, and wrangle large amounts of text quickly and accurately [1].

Performance: CLAUDE3 models have significantly improved in various tests, outperforming other LLMs like GPT-4 and Gemini Ultra on joint evaluation benchmarks[2].

Applications: CLAUDE3 can be used for operational efficiency, extracting relevant information from business emails and documents[1.]

Integration: To integrate CLAUDE3 into an application, one needs to set up a Console account and obtain an API key[3].

MISTRAL

Concept: MISTRAL is an open-source LLM that tackles various NLP tasks[4]. It stands out for its impressive performance, surpassing other 7 billion parameter language models[4].

Benefits: MISTRAL’s API allows developers to experiment with prompts and interact with the model[5].

Performance: MISTRAL models have been benchmarked against top-performing LLMs and have significantly improved [5].

Applications: MISTRAL can be used for classification, summarization, personalization, and evaluation[5].

Integration: To integrate MISTRAL into an application, one needs to set up an API key[6].

OPENAI

Concept: OpenAI offers a framework to evaluate an LLM or a system built on top of an LLM[7]. It provides an open-source registry of challenging evaluations[7].

Benefits: OpenAI’s continuous model upgrades allow users to efficiently test model performance for their use cases in a standardized way7.

Performance: OpenAI’s evaluation framework helps validate and test LLM applications’ outputs [7].

Applications: OpenAI’s evals can be used to measure the quality of the output of an LLM or LLM system[7].

Integration: To integrate OpenAI into an application, one needs to set up and specify their OpenAI API key[8].

GEMINI

Concept: Gemini is a series of multimodal generative AI models developed by Google[9]. Depending on the model variation, Gemini models can accept text and images in prompts [9].

Benefits: Gemini’s API allows users to use text and image data for prompting[9].

Performance: Gemini models are designed to perform vision-related tasks like captioning an image or identifying what’s in an image[9].

Applications: Gemini can generate text using text prompts with the gemini-pro model, and text and image data can prompt the Gemini-pro-vision model[9].

Integration: To integrate Gemini into an application, one needs to set up an API key[10].

Benchmark comparisons

Several benchmark comparisons have been conducted between CLAUDE3, MISTRAL, OPENAI, and GEMINI. Here are some key findings:

CLAUDE3

Anthropic, the company behind CLAUDE3, has shown, with the help of benchmarks across ten different evaluations, that CLAUDE3 beats both GEMINI and GPT-4 in every aspect. These aspects include undergraduate-level expert knowledge (MMLU), graduate-level expert reasoning (GPQA), basic mathematics (GSM8K), and more.

MISTRAL

While specific benchmark comparisons for MISTRAL are not mentioned in the search results, it’s worth noting that MISTRAL is an open-source LLM that tackles various NLP tasks. It stands out for its impressive performance, surpassing other 7 billion parameter language models.

OPENAI

OpenAI’s GPT-4o is a natively multimodal AI that can understand and generate content across text, images, and audio inputs. GPT-4o matches the performance of GPT-4 Turbo in text, reasoning, and coding intelligence but sets new benchmarks in multilingual, audio, and vision capabilities.

GEMINI

Gemini has been a formidable competitor in combining coding and textual understanding. Even though it performs well in visual tasks, Claude 3’s introduction has brought attention to specific areas that need work, particularly in tasks requiring accuracy and a better contextual understanding.

Please note that these comparisons are based on specific benchmarks, and the performance of these models can vary depending on the particular task and use case.

Case study

I developed several notebooks thoroughly tested in Google Colab to demonstrate the capabilities of the following LLMs: GEMINI[11], MISTRAL[12,12a], GPT-4o[13], and CLAUDE3[14].

Conclusion

These LLMs offer unique features and capabilities, making them suitable for various applications. Their APIs provide developers with the tools to integrate these powerful models into their applications, opening up a world of possibilities for natural language processing tasks.