6 key LLMs to power your text-based AI applications

Large language models (LLMs) have revolutionized the field of AI and natural language processing (NLP). LLMs are trained on massive datasets of text, containing millions or even billions of data points. These models have shattered barriers of what was once thought possible in natural language understanding and generation. 

To effectively assess the performance and reliability of powerful LLMs, it's crucial to have standardized methods for measuring a model's capabilities across complex tasks like code generation and reasoning.  This requires benchmarks, but traditional methods have faced criticism for issues like data contamination, inability to provide objective evaluations, scalability challenges, and other drawbacks.

Labelbox overcomes the limitations of traditional benchmarking with our human-centric evaluation approach through Labelbox Leaderboards. By leveraging our modern AI data factory—consisting of a robust platform, scientific processes, and human experts from our Alignerr network of domain and language specialists—we offer accurate assessments for nuanced tasks such as factual accuracy, contextual understanding, and advanced reasoning.

As a follow-up to 6 cutting edge foundation models for computer vision, this blog post dives into six popular LLMs and explores their intended use cases, limitations, and possible real-world use cases. With Labelbox’s data factory, you can explore, fine-tune, evaluate, compare, and leverage the LLMs listed below to accelerate model development. 

OpenAI ChatGPT

ChatGPT is a large multi-modal model that accepts both image and text inputs and provides text outputs. OpenAI has released a couple different model variations, with the most prominent being GPT-3, GPT- 4,  GPT-4o mini, and GPT-4o. Each generation of GPT models has built upon the successes of its predecessors, pushing the boundaries of AI and natural language processing. With improved accuracy, multimodal capabilities, and advanced reasoning skills, GPT has become increasingly human-like and versatile to broader applications.  

The latest version of GPT is a highly advanced model with capabilities in in-depth reasoning, multilingualism, and handling complex tasks like creative writing, literary analysis, coding, and structured scientific explanations. OpenAI also claims that their model generates text twice as fast and is 50% more cost-effective, all while enhancing its ability to perform more human-like behaviors. 

OpenAI has described their mission as striving to “ensure that artificial general intelligence—AI systems that are generally smarter than humans—benefits all of humanity.” Their goal is closely tied to developing models that prioritize human alignment and steerability, suggesting that safety is an integral part of their advancement process.  

While ChatGPT offers a wide range of capabilities and valuable applications, it still has some limitations. Since the model is trained to respond in the most likely way based on a prompt, it doesn't actually "know" anything and can sometimes generate inaccurate or hallucinated responses. For this reason, it's important to be selective about when to use the model for critical tasks. Additionally, ChatGPT can still produce non-original text or harmful content, which may inadvertently reinforce biases and contribute to other negative outcomes.

Sample use case:  

In customer support, GPT can power intelligent chatbots that provide 24/7 assistance, handling inquiries, troubleshooting common issues, and offering personalized responses based on customer data. Additionally, it can help automate the creation of knowledge base articles and FAQs, ensuring consistency and accessibility for both customers and support agents.

Google Gemini

Gemini is Google DeepMind's cutting-edge multimodal language model, excelling in text, image, audio, and video understanding. It pushes the boundaries of reasoning, multilingualism, function calling, and long-context performance. Google has released a line of versatile Gemini models from 1.0 Nano, 1.0 Ultra, 1.5 Pro, 1.5 Pro with Deep Research, and 2.0 Flash Experimental in the last couple of years. 

Gemini excels in tasks like code generation, delivering factually accurate responses, and solving complex math equations, making it ideal for a range of advanced technical use cases. With benchmark scores of 80% or higher in these areas, Gemini paves the way  for the development of AI agents capable of memorizing, reasoning, and planning to complete tasks on your behalf.

Although very powerful, the limitations for Gemini include struggles with creativity and reasoning with challenging datasets. Its capabilities around audio also could be improved with a lower benchmark score of 40.1% for automatic speech translation. 

Google seems focused on enhancing  Gemini’s  performance, reducing latency, and expanding the model’s native capabilities. 

Sample use case: 

In the finance industry, Gemini can extract key financial data, such as revenues, expenses, and budget figures, helping to identify inconsistencies or outliers. It can also streamline financial processes by categorizing transactions, automating routine tasks, and generating reports like expense summaries or P&L statements, saving time and improving efficiency.

Meta Llama

Meta, a leader in the open-source space, offers a range of Llama models—3.1, 3.2, and 3.3—each with distinct capabilities: multilingual support in 3.1 and 3.3, and multimodal features in 3.2. With the public release of these models, Meta aims to drive growth, foster exploration, and compete with leading closed-source LLMs in the space. 

Llama excels at instruction-following, long-context processing, coding, and multilingual translation tasks, with benchmark scores for these tasks ranging from the high 80s to the high 90s. During Llama’s research and iterative post-training process, Meta noted they prioritized human evaluation to have more accurate comparisons in real-world scenarios. 

Designed to offer developers a more versatile model option, Llama is the most popular open-source AI model. It was built to empower users to create custom solutions that align with their unique goals and ideas. It integrates seamlessly into a larger ecosystem, managing various components, including external tool integrations. This makes Llama an ideal choice for developers who need to fully customize an LLM for their specific needs, train it on new datasets, and perform additional fine-tuning.

Like other large language models, Llama's knowledge and capabilities are limited to what has been explicitly included in its training. As a result, it requires continuous fine-tuning with high-quality data; without this, biases and inaccuracies can emerge, particularly in niche areas where training data is scarce.

Sample use case: 

With Llama’s impressive multilingual capabilities, it can be used across a variety of real-world use cases such as language translation, question answering, and content creation. For example, Llama can be used to build a chatbot to handle customer inquiries across different languages. This can be particularly useful for retailers that have global customers or businesses that operate in multiple countries and need to communicate with people across different languages.

Anthropic Claude

Anthropic’s Claude is an LLM with exceptional vision capabilities and excels in advanced reasoning, language nuances, humor, and complex instructions. The latest version, Claude 3.5, is particularly strong at generating content with a natural, engaging tone, and, according to Anthropic, operates twice as fast as Claude 3 Opus, outperforming it by 26% on internal agentic coding evaluation tasks.

Claude 3.5 demonstrates strong performance across a variety of tasks, with benchmark scores of 93.7% in coding, 70.4% in vision question answering, and 88.3% in reasoning over text. These capabilities have made this model a popular choice for businesses with critical use-cases. 

Unlike some other AI models, Claude 3.5 cannot directly access the internet for real-time information, meaning its capabilities are limited to the knowledge built into its training. Additionally, users have reported occasional messaging limitations that can vary based on demand, potentially impacting accessibility.

Sample use case: 

For coding use cases, Claude 3.5 has the capabilities to support your entire software development lifecycle, from initial design and coding to bug fixes, maintenance, and optimization. It also excels at automating repetitive tasks like code refactoring and testing, improving productivity and consistency. This can be useful in significantly accelerating your development process and reducing errors.

xAI Grok

Built by xAI, Grok is a newer player in the LLM space, but it has already made significant strides in reasoning, coding, chat, and vision capabilities. What sets Grok apart is its focus on delivering responses with humor and wit—an emphasis not commonly found in other LLMs.

Available on X and inspired by characters like the Hitchhiker's Guide to the Galaxy and JARVIS from Iron Man, Grok is advertised as an “AI assistant with a twist of humor and a dash of rebellion” and is useful for completing tasks around answering questions, problem solving, and brainstorming while keeping you entertained. 

Through human evaluators called “AI tutors,” responses generated by Grok are evaluated in two main areas: instruction following and factuality. The model is also evaluated against various academic benchmarks and has achieved performance levels on par with other leading LLMs, particularly excelling in areas like science, general knowledge, and math, while outperforming competitors in vision-based tasks.

Since Grok is still in the early stages of development, it may occasionally provide factually incorrect information, misinterpret details, or miss important context. It also lacks the multimodal and voice capabilities that many of its competitors currently offer.

Sample use case: 

For e-commerce use cases, Grok can power recommendation engines by analyzing customer preferences and browsing behaviors to suggest products, services, or content. Whether integrated into e-commerce websites, streaming platforms, or learning management systems, it can offer users tailored suggestions that enhance engagement and drive sales.

 Mistral Pixtral Large

Pixtral Large is an open-source, multimodal model built on Mistral’s Large 2, combining its robust text capabilities with new features like multilingual OCR and the ability to understand images, including charts. 

When evaluated against key benchmarks, Pixtral outperforms in complex mathematical reasoning with visual data, along with improvements in long-context understanding and more accurate function calling, making it particularly well-suited for agentic workflows. This makes it a favorable choice for enterprise use cases such as knowledge exploration and sharing, semantic understanding of documents, task automation, and improved customer experiences. 

Designed to prioritize co-designing models and product interfaces, Pixtral was trained with high-impact front-end applications in mind. For example, Pixtral Large powers Mistral’s AI tool, Le Chat, which integrates text, vision, and interactive functionalities into a unified platform, making it ideal for diverse use cases like research, ideation, and automation.

However, as a relatively new player in the space, Pixtral Large lacks the established infrastructure and years of fine-tuning that other LLMs benefit from. This means it may require time to build user trust and could still experience occasional inaccuracies, hallucinations, and glitches.

Sample use case: 

In the legal industry, Pixel Large can streamline automation by analyzing large volumes of legal documents, contracts, and case files to extract key information, summarize content, and identify relevant precedents. This can significantly reduce manual review time, improve accuracy in legal research, and help law firms manage complex cases more efficiently.

Explore LLM Development with Labelbox

Large language models have changed the way AI builders and business users build more powerful models. You can now leverage cutting edge LLMs to accelerate the development of common enterprise AI applications at scale across a variety of industries.

 Looking to unlock the full potential of LLMs? 

• Leverage Labelbox’s end-to-end platform and network of expert human evaluators across diverse domains and languages to run a fully functional data factory for advanced LLM training.
• Explore our leaderboards and view the latest evaluations across AI modalities and applications.

If you’d like to evaluate your LLM as part of the next leaderboard update or have a question, feel free to contact us here.

Sources

Anthropic. (n.d.). Claude Sonnet. Anthropic. https://www.anthropic.com/news/claude-3-5-sonnet

DeepMind. (n.d.). Gemini. DeepMind. https://deepmind.google/technologies/gemini/

DeepMind. (n.d.). Gemini Pro. DeepMind. https://deepmind.google/technologies/gemini/

DeepMind. (2023). Gemini v1.5 report. DeepMind. https://arxiv.org/abs/2403.05530

Llama. (n.d.). Llama models. Llama. https://www.llama.com/

Mistral AI. (2023, December 4). Pixtral Large: The next step in generative AI. Mistral AI. https://www.infoq.com/news/2024/12/pixtral-large-mistral-ai/

OpenAI. (n.d.). GPT-4: Model overview. OpenAI. https://platform.openai.com/docs/models/gp

OpenAI. (2023, January 30). Planning for AGI and beyond. OpenAI. https://openai.com/index/planning-for-agi-and-beyond/

VentureBeat. (2023, December 4). Mistral unleashes Pixtral Large and upgrades Le Chat into full-on ChatGPT competitor. VentureBeat. https://www.perplexity.ai/page/mistral-releases-pixtral-large-TnUT77WxSueYxDHNAteYGw

xAI. (n.d.). About Grok. xAI. https://x.ai/

xAI. (n.d.). About xAI. xAI. https://x.ai/about

xAI. (2024, March 18). Grok 1.5v release. xAI. https://x.ai/