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 FactsBERT 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 DevelopmentBERT 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.
BenefitsClaude 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 DevelopmentClaude 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.
BenefitsCoral 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 DevelopmentCoral 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.
BenefitsDBRX 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 DevelopmentDBRX 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.
BenefitsGoogle’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 DevelopmentGemini 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.
BenefitsGemma 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 DevelopmentGemma 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.
BenefitsAlso check out: 10 AI-Powered API Security Tools
GPT-4 Quick FactsPerhaps 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 DevelopmentGPT-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.
BenefitsLlama 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 DevelopmentLlama 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.
BenefitsMistral 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 DevelopmentMistral’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.
BenefitsPaLM 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 DevelopmentPaLM 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.
BenefitsStability 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 DevelopmentThe 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.
BenefitsStable 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 DevelopmentStable 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.
BenefitsWhile 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.
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