We've come a long way since GPT-3 first took the world by storm. Now, LLMs are used daily for various tasks, and there are around 40 options available! This post explores the strengths and weaknesses of these top contenders.
But in this rapidly evolving landscape, three models from technology heavyweights stand out: Llama 3, GPT-4, and Gemini. Below, we’ll explore the nuances and performance comparisons of these top LLMs.
Understanding the LLM Versions:
What is Llama 3?
Meta’s flagship LLM includes model weights of 8B, 70B, or 400B parameters. It’s especially suited for complex tasks such as those involving creativity and problem-solving. Known for its creative and nuanced responses, Llama 3 excels in generating engaging storytelling and entertainment content.
Llama 3 is particularly good at coding and offers an API to help users build and scale generative AI applications. While it only offers textual inputs and outputs, Meta has indicated that a multimodal version of Llama 3 is in the works. Meta claims Llama 3 70B outperformed Gemini Pro 1.5 in the MMLU benchmark, indicating the model’s general knowledge level.
What is GPT-4?
OpenAI’s GPT-4 builds on the foundation of its predecessors, offering significant improvements in performance and accuracy. GPT-4 Turbo enhances these capabilities further, including a better knowledge cutoff and improved processing speeds. GPT-4 Omni, the top performer in many benchmarks, boasts twice the speed, half the price, and higher rate limits than Turbo.
Known for its strong natural language understanding capabilities, GPT-4 discerns context and nuance in conversations exceptionally well. Its inputs are primarily text-based, but it can also leverage image inputs with GPT-4 with vision (GPT-4V). However, it is not without flaws; some users find its responses overly verbose and indirect.
What is Gemini?
Google's Gemini stands out for its ability to use multiple data sources, such as Google search, when considering responses. This is a step up from GPT-4, which relies on its training data unless explicitly asked to search the web. Previously known as Bard AI, Gemini allows users to tailor responses in terms of length, detail, and tone. It offers text, image, and audio inputs, making it the most multimodal of the three.
Despite these strengths, Gemini has faced criticism for occasionally refusing to answer queries and not being entirely transparent about why. However, its adaptability and user feedback mechanisms are significant advantages.
Benchmark Performance:
The provided chart highlights how these models performed on various benchmarks, with GPT-4 Omni excelling in four categories. However, Llama 3 and GPT-4 Turbo each hold the top spot in the remaining two benchmarks.
Key Points About Each Model:
- Llama 3: excels in creativity, problem-solving, humor, and coding, offers various sizes, and boasts a multimodal version in development.
- GPT-4: shines in natural language understanding, offers customization options, and has multiple versions, but can be indirect and lacks multimodality (except for GPT-4v).
- Gemini: a crowd favorite for using multiple sources, offers user feedback options, allows response customization, and excels in multimodality, but can be hesitant to answer and unclear about reasons.
Conclusion:
Choosing the best LLM depends on your specific needs. Each offers unique strengths and weaknesses. Consider factors like:
- Task: What do you need the LLM to do?
- Data Sources: Does the LLM consult external sources for information?
- Multimodality: Does it accept and generate different data types (text, image, audio)?
- Transparency: Can you understand how the LLM arrives at its answers?
Regardless of the model you decide to explore, you can rely on the AI and data science experts at Dataknox Solutions to assist you in conceptualizing, developing, and deploying your next LLM-based application. Reach out to us today to schedule a personalized discovery call and let us support you in scaling your next innovative project.
