12 Top Performing LLMs In 2024 Posted in Platforms Kristopher Sandoval September 17, 2024 The world of artificial intelligence and large language models (LLMs) has rapidly grown in a very short time. It’s grown so quickly, in fact, that developers might find the number of options absolutely dizzying. To that end, it makes sense to analyze popular LLMs in the market and how they compare. Below, we look at some of the top-performing LLMs in 2024. We’ll examine the pros and cons of each LLM. And given the synergies between AI and APIs, we’ll consider how each could aid API development. BERT Quick Facts Developer – Google Website – BERT GitHub Best Uses for BERT BERT is the result of an effort by Google to create an LLM focused on answering questions. Google developed BERT to create a model that would allow for training with large bodies of text. They specifically sought to make an unsupervised training model that could process the plain text openly available on the internet. The resultant LLM is transformer-based, trained on more than 340 million parameters, and extensible to a variety of tasks, including sentiment analysis, question answering, and context inclusion. How BERT Can Help API Development BERT is a great solution for specialized LLM training, especially when that training is multilingual and has variable data types that need processing. BERT is particularly adept at specific use cases it can be fine-tuned to, so for API development on specific niche functions or data sets, BERT could be a huge boon to processing and remixing. Benefits BERT is a state-of-the-art model for natural language processing and has been trained on a large amount of multilingual data. Google provides BERT with pre-trained models, making it easy to train the system on your own data sets for fine-tuning to specific tasks. Claude 3.5 Quick Facts Developer – Anthropic Website – Claude Website Best Uses for Claude 3 Claude is a very powerful LLM focused on advanced reasoning. This focus unlocks various features, ranging from vision detection and object analysis, code generation, live translation, and more. This has made Claude attractive for conversational AI solutions, such as customer service chatbots, interactive agents, AI assistants, and so forth. It has also made it a contender for multilingual use cases requiring more advanced and contextual processing. How Claude 3 Can Help API Development Claude is highly conversational and works really well on complex routing. For this reason, Claude’s biggest benefit to API development may be in the planning and design phase, allowing for creative ideation and development at scale with auto-generative code, stub creation, and API testing. Benefits Highly conversational and adaptive to different contextual use cases, making it prime for “human-like” interactions needing LLM processing. Because of this “human-like” capability, it’s often good at routing through complex conversations or questions that deviate from the initial prompt. Also read: 7 Ways to Test LLMs Coral Quick Facts Developer – Cohere Website – Cohere Blog Post Best Uses for Coral Coral is an LLM focused primarily on assistant-like behavior. Employees within a company could ask a system like this for answers instead of tracking down answers from coworkers, unlocking massive productivity outcomes. Cohere developed Coral for this specific use case, providing an LLM that is customizable, private, and contextually relevant. How Coral Can Help API Development Coral works well in providing contextual backing for its proclamations. To that end, it might work best as an assistive tool in API development by providing insights for further development and extension of the extant codebase. This benefit can also help make for more useful testing and vulnerability scanning. Benefits Designed to provide context to answers for usability in enterprise environments. Accordingly, Coral’s potential to hallucinate is easier to manage as you only need to check the sources. Highly private by design due to its enterprise focus. DBRX Quick Facts Developer – Databricks Website – Blog Announcement Best Uses for DBRX DBRX is a powerful LLM from Databricks. An open model, it is directly competing with GPT and Gemini, and notes in its documentation that it has actually exceeded those models in some areas. DBRX is highly capable in code generation, and boasts decent processing for complex inference and problem solving. Notably, it is also high-efficiency with a mixture-of-experts architecture, allowing for quick and low-cost collation of contextual data and output in a short timeframe. How DBRX Can Help API Development DBRX is especially good at complex inference and, as such, offers a solution for inferred vulnerability detection, heuristics tracking, and security posture assurance. Using DBRX to scan through live code and logs can help surface potential vulnerabilities at scale and expose significant security holes. Benefits A good generalist LLM with highly specialized domains of knowledge included, offering a jack-of-all-trades solution. While its API and platform solution are locked to the Databricks platform, you can freely download the model for local use. Gemini Quick Facts Developer – Google Website – Gemini Landing Page Best Uses for Gemini Google’s Gemini is a complex AI for natural language processing, conversational computation, visual processing, and more. It is very much a generalist AI solution. While anyone can use this power at scale, Gemini suffers from some of the same pitfalls that other generalist AI such as ChatGPT do — notably, it still suffers from hallucinations. That said, it’s highly customizable to your specific use case and is a great tool. How Gemini Can Help API Development Gemini has powerful visual compute processing power, and can be integrated with APIs for detection. For enterprise APIs, this can take a powerful role in quality assurance, providing new functions that would require intense training or data sets that are highly specialized and expensive. Benefits Backed by Google’s development experience and tooled for conversational computation and development. Relatively good balance between performance and resourcing, especially when used as an external resource. Also read: Comparing Top AI Code Assistants: A Comprehensive Review Gemma Quick Facts Developer – Google Website – Gemma Landing Page Best Uses for Gemma Gemma is a series of derivative open models from the Gemini codebase designed to provide general-purpose and efficient models for specific tasks. Gemma 2 is a general-purpose LLM providing variable parameter sizes, whereas Gemma 1 is a lightweight text-to-text model designed to provide simple output for complex tasks. RecurrentGemma is an additional model providing recurrent neural network access with an eye for improved memory efficiency. PaliGemma focuses on vision-language tasks, allowing for complex visual computation. Finally, CodeGemma is focused entirely on code auto-completion and generation. How Gemma Can Help API Development Gemma is widely applicable to a range of use cases. While it’s not a model that excels at anything in particular compared to other models, it represents a one-stop shop for integration that can boost any API to a higher state with very little overhead and cost. Benefits High versatility through multiple model platforms means that Gemma has a solution for most use cases. Also check out: 10 AI-Powered API Security Tools GPT-4 Quick Facts Developer – OpenAI Website – GPT-4 Landing Page Best Uses for GPT-4 Perhaps the most well-known on this list, OpenAI’s GPT-4 is credited by many as leading the industry’s focus on AI and LLMs, and for good reason. GPT, in all of its versions, has been at the forefront of logical reasoning LLMs, providing decent results across a wide range of use cases. While older versions suffered from hallucinations and quality concerns, GPT-4 has advanced on this considerably. How GPT-4 Can Help API Development GPT-4 is, in many ways, the bleeding edge of LLM power. Its customization potential allows programmers to quickly develop and deploy it for particular iterations at scale. Integrating with GPT-4 might carry a hefty usage cost. Still, it unlocks incredible operations at a scale that can offload what would be a resource and cost-heavy processing to OpenAI’s proven corpus of functions. Benefits GPT-4 is considered state of the art for many generative tasks, offering incredibly powerful computation and remixing capabilities. GPT-4 is only one of several models, and GPT offers specific training and widget creation for specific model needs. Llama 3.1 Quick Facts Developer – Meta Website – Llama Landing Page Best Uses for Llama 3.1 Llama is a relatively well-known model due to its high customization. Meta developed Llama to handle a wide variety of tasks from video generation to text creation from prompts, training it on a wide variety of datasets to provide compelling results across diverse use cases. Notably, it is fully open source and is designed for adaptation and customization from default, making it a strong offering for bespoke solutions. How Llama 3.1 Can Help API Development Llama offers high customization and portability, allowing it to be deployed across various environments. Although it’s an imperfect model, it fits well with various use cases for relatively low cost across multiple platforms like AWS and Google Cloud. Benefits Open-source and accessible with clear price estimates via partnered platforms allows consumers to get a firm grasp of what their use might cost. Strong performance across a very wide range of purposes and form factors. Mistral Quick Facts Developer – Mistral Website – Mistral Models Best Uses for Mistral Mistral is designed to fit two specific use cases. With Mistral Nemo, a small model can be deployed for efficient task processing with minimal resourcing, and with Mistral Large 2, more complex tasks can be handled across a variety of inputs and output formats. Mistral focuses on portability and customization, making their models best for organizations needing high customization across specialized technical domains. How Mistral Can Help API Development Mistral’s major selling point for most APIs will be in Mistral Nemo. Nemo is a powerful LLM with very low resource need, and as such, it’s a great fit for IoT devices and other minimal compute systems. While many of the LLMs on this list require heavy resource allocation, Nemo is a great option for low-resource environments. Benefits Optimized for efficiency, speed, and portability, allowing either a focus on power or on lightweight processing. Mistral Large 2 deals well with complex machine-oriented problems including data transformation and code generation. PaLM 2 Quick Facts Developer – Google Website – PaLM 2 Website Best Uses for PaLM 2 PaLM 2 is a language model focused on contextual data and reasoning. Notably, it has been tooled specifically for problem-solving, with significant abilities demonstrated in code generation and machine learning. PaLM 2 has been trained on multiple data sets, and with inclusion of source code, this has resulted in the ability to generate relatively high quality and stable code as well as debug code when presented. How PaLM 2 Can Help API Development PaLM 2 is particularly adept at code generation, more so than others on this list. While other LLMs are good for specific parts of the API-as-a-product cycle, PaLM 2 is useful for actually generating boilerplate, stubs, servers, clients, and more. Benefits Uniquely designed for code, problem-solving, complex questions, and other prompts that typically stymie other models. Wide support for data input types and styles. Stable Beluga 1 & 2 Quick Facts Developer – Stability AI Website – Stability AI Announcement Best Uses for Stable Beluga 1 & 2 Stability AI Beluga 1 and 2 are open-access LLMs provided by Stability AI with a focus on complex and intricate tasks within specialized domains such as law or mathematics. Stability AI has released the models in an effort to push open LLM research to more specialized domains and topics. How Stable Beluga 1 & 2 Can Help API Development The best value offering for Stable Beluga is in the assurance of compliance between APIs and the data being processed. Its specialized knowledge of regulatory text means that vulnerabilities can be scanned and compared against regulatory requirements at scale with a reasonable expectation of quality and accuracy. Benefits Beluga is well-suited for specialized topics and domains, making it more useful than other LLMs that may not be trained on relevant data sets. Stable LM 2 1.6B Quick Facts Website – Stability AI Blog Post Best Uses for Stable LM 2 1.6B Stable LM is unique in the field of LLMs as it bills itself as something very different — a “Small Language Model.” In essence, Stable LM approaches the AI field with an eye for providing a very small but efficient model that can be used by lower-tier hardware. While the core model is pre-trained, it can be optimized to specific use cases by adopting developers. How Stable LM 2 Can Help API Development Stable LM, like Nemo, is a good choice for small IoT or other efficient environment systems. To that end, it should be considered for all IoT and minimally-resourced APIs. Benefits Stable LM is designed to be much more lightweight and efficient than others on this list. Because it is smaller and more resource-conscious, it is a good option for IoT and other constrained environments. Final Thoughts on Top LLMs in 2024 While all of the LLMs on this list offer unique attributes that make them a solid option for generalist APIs, there are certainly specific use cases for each where they truly shine above the rest. For that reason, including an LLM in your API or its development process should be a consideration of form and function above anything else. Did we miss any major LLM solutions? Please let us know in the chat below. The latest API insights straight to your inbox
