Artificial Intelligence
Comparing the Stats of Top Large Language Models: Google, OpenAI, Microsoft, and Amazon (June 2024)
Sam Brown
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June 3, 2024
Artificial intelligence is advancing rapidly, with some of the biggest tech companies leading the charge with their sophisticated language models. Recently, Google, OpenAI, Microsoft, and Amazon-backed Mistral have all announced updates to their models. This blog post compares the latest from these companies, including OpenAI's new GPT-4o, to see how they measure up.
I did all the digging so you don’t have to. Let’s unpack it!
Google’s PaLM 2: The Multilingual Expert
Size: 540 billion parameters (a measure of the model's complexity).
Training: Trained on over 100 terabytes of data, including many languages and scientific texts.
Performance: Scores very high on language tests, showing great understanding of multiple languages and scientific information.
Uses: Excellent for translating languages, understanding scientific papers, and even writing code.
OpenAI’s GPT-4 and GPT-4o: Versatility and Efficiency
GPT-4 Size: 175 billion parameters.
GPT-4o Size: Optimized for efficiency with fewer parameters but enhanced performance metrics.
Training: Both models use vast datasets covering a wide range of topics and text sources.
Performance: GPT-4 scores high on language tests, demonstrating strong performance in understanding and generating text. GPT-4o, while smaller, maintains high performance with improved efficiency and response times.
Uses: Versatile, handling chatbots, creative writing, general information tasks, and now optimized for faster deployment with GPT-4o.
Microsoft’s MT-NLG: The Technical Pro
MT-NLG (Megatron-Turing Natural Language Generation) Size: 530 billion parameters.
Training: Built using a broad dataset that covers many languages and technical domains.
Performance: Excels in detailed content and technical language tasks, performing exceptionally well on benchmarks like LAMBADA (Linguistic Acceptability), BoolQ (Reading Comprehension), and RACE-h (Reading Comprehension).
Uses: Ideal for technical documentation and complex content creation, especially for businesses.
Amazon-backed Mistral: The Specialist
Size: 200 billion parameters.
Training: Focused on high-context and specialized data.
Performance: Scores highly in specific areas like finance, healthcare, and legal texts.
Uses: Best for specialized industry tasks where detailed and specific knowledge is crucial.
Comparison Overview
Google’s PaLM 2: Great for multilingual tasks and scientific research.
OpenAI’s GPT-4 and GPT-4o: Versatile models suitable for a wide range of purposes, with GPT-4o offering optimized efficiency.
Microsoft’s MT-NLG: Strong in technical and detailed content, excellent for business applications.
Amazon-backed Mistral: Excels in specialized areas like finance, healthcare, and legal tasks.
I've been enjoying and using OpenAI’s GPT-4o the most and find it to be an excellent value. Each model has its unique strengths and is best suited for different kinds of tasks, offering distinct benefits depending on specific needs.
Sources
To ensure the accuracy and credibility, information has been sourced from the following articles, technical specifications, and industry analyses. For further details, readers are encouraged to consult the following resources:
Google’s PaLM 2 updates
OpenAI’s GPT-4 and GPT-4o updates
Microsoft’s MT-NLG updates
Mistral updates
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