Narus | Choosing the Best LLM for You

Until recently, large language models (LLMs) were not widely recognised by the public, but today understanding which one is right for you is a question many businesses are asking. Increased access to LLMs has sparked imaginations about how it could supplement, or even replace, various aspects of our personal and professional lives. Initially, these models were general-purpose, capable of tackling a wide array of writing tasks. But as technology advances, we've witnessed the emergence of more specialised models, each with unique strengths tailored to specific applications, such as coding, translation, and even research, so the benefit of multiple LLMs is becoming clearer.

In this guide to choosing the best LLM for you, we have looked at five LLM providers and their models, and five LLM use cases. As with many business processes, this is not an exact science, but by focusing on accuracy, speed, cost, and specific strengths during your decision-making, you can structure your approach, even though many elements might be subjective. All of which will help you get closer to identifying the LLM that best suits your needs. Ready to get started?

So… what is an LLM exactly?‍

A subset of generative AI (GenAI), LLMs are sophisticated artificial intelligence systems designed to understand and generate human-like text. By analysing extensive amounts of written content, they learn patterns in language, enabling them to predict and produce coherent and contextually relevant output. This ability to mimic human conversation and writing has made them invaluable across diverse fields, from customer service to content creation and beyond.

In a short period, GenAI has matured to the point of becoming indispensable for many businesses globally. This brings new considerations for IT managers, such as security and efficiency.

Even with deployment sorted, identifying the best LLM for your specific use case can be challenging. For generalised tasks, such as grammar checking or creating rudimentary copy, any currently available model will likely suffice. This article aims to guide you through your LLM choices by outlining the features and benefits of specialised LLMs, helping you harness their power to meet diverse needs.

How to compare LLMs

Comparing LLMs requires research. Consider your specific use case and evaluate each model’s performance in terms of accuracy, speed, and cost, as well as their particular strengths, such as creativity or coding skills, to determine the best fit. We are now seeing specific LLMs target very different use cases, with some becoming exceptional at individual tasks, while others offer a more generalised, all-round balance of capability and affordability.

In the following sections, we delve into the specifics of five LLM providers – OpenAI, Google, Mistral AI, Meta AI, and Anthropic – offering insight into their unique models and how they can be applied.

LLM providers and their models

OpenAI: GPT-3.5 Turbo, GPT-4, GPT-4o, GPT-4o Mini, GPT-o1 Mini, GPT-o1 Preview

Google: Gemini 1.0 Pro, Gemini 1.5 Flash, Gemini 1.5 Pro, Gemma 2

Mistral AI: Mistral 7B

Meta AI: Llama 2, Llama 3, Llama 3.1, Llama 3.2

Anthropic: Claude 3 Haiku, Claude 3.5 Haiku, Claude 3.5 Sonnet

Each of these LLM options are fine-tuned for specific purposes. Which one is the perfect fit for your needs? Let's explore some common use cases and discover the ideal LLM for each.

Use case 1: Which LLM should I use for code generation?

Code generation has emerged as one of the most popular applications for generative AI, with major players like Microsoft integrating Copilot, powered by ChatGPT-4, directly into GitHub to streamline the coding process. For tasks requiring precision and efficiency, GPT-4o is a standout choice. Its impressive performance in coding benchmarks offers a great balance of efficiency and cost-effectiveness, making it suitable for a wide range of coding projects, even if it occasionally encounters accuracy issues.

For developers tackling intricate challenges, GPT-o1 Mini excels in complex reasoning and problem-solving. It's a valuable coding assistant, although it may sometimes produce errors in more complicated scenarios. Meanwhile, Claude 3.5 Sonnet is renowned for its advanced code generation and debugging capabilities, providing a reliable option for those seeking a premium solution.

If you're seeking a more lightweight, open-source alternative, Mistral 7B is worth considering. It offers efficiency and the benefits of customisation, making it a flexible and adaptable choice for many developers. While it may not handle complex coding scenarios as adeptly as some larger models, it remains a practical option for those prioritising flexibility.

Use case 2: Which LLM should I use for translation?

When it comes to translation, selecting the right LLM can significantly impact the quality and efficiency of your work. Among the leading options, GPT-4o, Gemini 1.5 Pro, and GPT-4 each offer distinct advantages tailored to different translation needs.

GPT-4o is ideal for real-time translation, such as live conversations and translating user-generated content. Its strength lies in its exceptional contextual understanding, combined with accuracy, speed, and multilingual support. This versatility makes GPT-4o a compelling choice for dynamic translation tasks, supporting more than 50 languages thanks to its advanced neural network architecture.

For tasks involving long texts and documents, Gemini 1.5 Pro is an excellent choice due to its large context window. Additionally, its ability to handle interleaved image and text generation is particularly beneficial for multimedia translations. Supporting over 35 languages, Gemini 1.5 Pro is available to users worldwide, making it a solid choice for teams, wherever they work.

If you prioritise the highest translation quality, GPT-4 is the model to consider, despite its higher cost. It delivers exceptional quality and handles complex language tasks with ease, making it perfect for demanding projects where precision is paramount. Its ability to understand context and cultural nuances ensures translations are both accurate and meaningful to your audience.

GPT-3.5 Turbo is known for its zero-shot translation capabilities, meaning it can often provide decent translations without prior training on specific language pairs. This adaptability is useful for translating less common languages or for quick, informal translations. However, it may struggle with complex sentence structures and idiomatic expressions, and might not be as consistent as more advanced models like GPT-4 or Gemini 1.5 Pro, especially with longer texts or specialised domains.

Meta AI's Llama 3.1 is touted as a leading open-source model for translation, supporting eight languages. While it offers a promising alternative, further investigation is needed to fully assess its strengths and weaknesses in translation.

Use case 3: Which LLM should I use for data analysis and extraction?

For data analysis and extraction, several LLMs stand out, each with unique strengths.

GPT-4, with its multimodal capabilities, excels in processing diverse data types such as text and images. It's particularly adept at analysing financial data, identifying trends, and even generating code for data-related tasks. With reasoning abilities that rival human analysts, GPT-4 is a top choice for complex data analysis.

For those seeking versatility, GPT-4o builds upon the strengths of GPT-4 with enhanced multimodal capabilities and faster processing. It's a general-purpose model that handles a wide range of tasks, including natural language processing, code generation, and multimodal applications. Its ability to transform data and its multilingual support make it ideal for extracting information from complex documents and analysing global datasets.

Gemini 1.5 Pro shines when dealing with vast amounts of information and complex reasoning. It's perfect for analysing large codebases, processing extensive documents, and performing intricate reasoning across different modalities. This makes it well-suited for in-depth data analysis and extraction from diverse sources.

Llama 3 is another strong contender, thanks to its advanced architecture and extensive training data. It excels in understanding and generating human-like text with high accuracy, making it useful for tasks like market research, sentiment analysis, and competitive intelligence. Its optimisation for speed and efficiency enables it to handle large datasets and automate data analysis tasks effectively.

If deep reasoning in STEM fields is your priority, GPT-o1 is a specialised option. Although potentially slower and more costly than GPT-4o, it offers profound insights in scientific and technical domains. However, for general-purpose and multimodal tasks, GPT-4o strikes a better balance between speed, versatility, and cost-efficiency.

Use case 4: Which LLM should I use for content creation?

Content creation is a cornerstone of effective communication. Whether crafting compelling narratives or generating informative articles, the demand for high-quality written content is ever-increasing. LLMs can produce nuanced, contextually relevant, and engaging content, making them ideal for various tasks – from blog posts and articles to marketing copy and creative storytelling.

Currently, any LLM can generate usable written content for marketing campaigns and even summarise or repurpose existing content for different audiences. Ultimately, the LLM you choose may come down to personal preference, though factors like speed, cost, and specific project needs should be considered, as different LLMs have varying strengths.

Known for its advanced language capabilities, GPT-4 excels in generating high-quality essays, stories, and scientific writing. Its improved contextual understanding and creativity make it a top choice for tasks requiring depth and nuance. Additionally, its ability to process both text and images adds versatility. However, GPT-4 can be slower and more expensive than some alternatives.

Google's Gemini 1.5 Pro is frequently lauded for handling long contexts and generating creative text formats. It's a versatile option for complex tasks, thanks to its accurate and relevant responses. While it might require specific prompts and have slower response times, its overall performance in generating high-quality content is impressive.

If efficiency and speed are paramount, Mistral 7B is a compelling choice. Its impressive speed and cost-effectiveness make it ideal for tasks with quick turnaround times. Although it may have limitations in knowledge capacity and prompt injections, its overall performance makes it a viable option for many content creation tasks.

The best LLM for content creation will depend on your project's specific requirements and priorities. By understanding the strengths and weaknesses of each model, you can make an informed decision that enhances your content creation efforts.

Use case 5: Which LLM should I use for real-time applications?

Choosing the best LLM for real-time applications hinges on finding the optimal balance between speed, cost, and capabilities. In situations that require immediate responses, such as chatbots, virtual assistants, or live translation, generating text quickly and efficiently is crucial. Let's explore some leading contenders in this space:

OpenAI's GPT-3.5 Turbo is popular for real-time applications due to its speed and cost-effectiveness. This model is well-suited for conversational tasks, generating human-like responses with minimal delay. However, it has limitations, such as a knowledge cut-off date in September 2021, and potential struggles with consistency in longer responses or when synthesising information from multiple sources.

Google's Gemini 1.5 Flash is another strong option, especially for applications requiring long context handling and high-volume tasks. This model is optimised for speed and efficiency, boasting a massive context window of 1 million tokens. This makes it adept at retaining and utilising information, essential for tasks involving long conversations or documents. However, users should be aware of potential rate limits.

Anthropic's Claude 3 Haiku is noteworthy for its exceptional speed, making it the fastest and most affordable model in the Claude 3 family. Its speed, combined with vision capabilities, makes it a versatile choice for real-time applications. Despite its strengths, it does have context window limitations.

The best choice depends on your application's specific needs. If you prioritise speed and cost, GPT-3.5 Turbo is a reliable option. For applications needing extensive context handling, Gemini 1.5 Flash excels. Meanwhile, Claude 3 Haiku offers unmatched speed and affordability.