Not all large language models (LLMs) are the same. With so many models available and new ones being introduced all the time, choosing between them is challenging. In this article, I provide a framework on how to compare the options and make the best choice.
Selecting the wrong LLM could mean poor performance or a poor user experience - the model might be too slow for your needs or even provide inaccurate results. Starting an AI project with a model that doesn't fit could also mean your initiative consumes budget and resources, but fails to even make it through development.
Among the main things to consider when choosing a model or LLM-powered tool are the nature of your problem, the data it requires, and the depth of reasoning needed. Some models excel at pulling real-time facts from the web. Others shine at lengthy analytical reasoning or working with private proprietary data.
You need to start with a solid understanding of your use case, including the business objectives and the specific problems you are aiming to solve. Then review the available LLMs, analyzing their strengths and weaknesses and evaluating them against the following factors.
Key factors in choosing an enterprise LLM
Data source and freshness: Does the model have access to real-time information, such as live web data or news? Or is it limited to a static training dataset? For example, Grok (Live Search), Perplexity (which is primarily an answer engine that orchestrates models with live web retrieval and citations, rather than a single stand-alone foundation model), and Gemini (grounded with Google Search), can provide up-to-the-minute answers, while other models rely on fixed knowledge cut off dates. Gemini's Google Search grounding can cite verifiable sources beyond the underlying model's cut off.
GPT-4o's training data is currently only up to October 2023 (although the ChatGPT chatbot layers search functionality on top of the model to pull in up-to-date information from the web when a user prompt or question requires it).
So, consider whether your use case requires access to the latest information or can function with knowledge from October 2023 or earlier, supplemented by any data you can manually provide. ChatGPT Enterprise, for example, states it does not train on your business data and is SOC 2 compliant, so any data you supplement it with remains private.
Reasoning depth and quality: Different LLMs have varying levels of reasoning and analytical capabilities. Top-tier models, like GPT-5, OpenAI o3, and Claude Sonnet 4.5 are known for their complex reasoning, coding, and problem-solving skills. Others may prioritize speed or concise answers over deep reasoning. If your use case involves quick fact-finding such as retrieving a brief answer or summary, a lighter model might suffice. However, for deep analysis, such as multi-step reasoning, long-form reports, or complex decision support, you'll want a model proven to handle higher-order logic and large contexts.
Data privacy and control: Consider the sensitivity of your data and the confidentiality requirements surrounding where it will be processed. Some solutions, like ChatGPT via OpenAI's cloud, process data on the provider's servers, though enterprise plans typically include assurances that your data won't be used to train their models.
Other models, such as IBM's Granite series or open-source LLMs like Llama 3.1 (openly available,, self-hostable), can be deployed on-premises or within a private cloud, offering greater control over data and compliance. If data residency, privacy, or IP protection is a top concern, the ability to self-host or use a dedicated instance is critical.
Data transparency and governance: Many of the largest foundation models have been trained on examples from the internet, making it hard to be certain exactly what data they were trained on. This lack of transparency is concerning, especially if you are worried about the possibility of copyrighted material being used or the risk of bias, hallucinations, or even abusive language cropping up in outputs.
If you want to use AI for enterprise applications, you might prefer models that are explicitly trained on known enterprise datasets - like the Granite models. Granite models emphasise transparent, vetted training data and filtering (e.g. removal of copyrighted/sensitive content and objectionable material) and are designed to meet IBM AI Ethics and Chief Privacy Office criteria.
Customization and integration: Consider how easily the LLM can be fine-tuned or integrated with your existing systems. Some models provide APIs and support fine-tuning with your proprietary data. For example, OpenAI supports fine-tuning for GPT-4o (and 4o-mini); IBM watsonx supports fine-tuning/adapter approaches for Granite. Others might be "off-the-shelf" only.
Also, evaluate the deployment options. Are they available as SaaS platforms, cloud services (AWS, Azure, GCP offerings), or downloadable models? And consider compatibility with enterprise software you might use, such as Microsoft Teams, Slack or CRM systems. Don't underestimate the complexity of integrating LLMs into existing systems, which can create significant challenges later on.
Model size: Do you need the broad, generalized knowledge and capabilities that large LLMs can provide? Or will your needs be better served by smaller, fit-for-purpose models fine-tuned for your specific use cases and business or industry sector? Smaller models require less compute power, energy and memory, using fewer graphics processing units (GPUs) and other data center resources. This can make them more cost-effective, depending on your use case. Having fewer parameters typically enables faster processing times, allowing smaller models to respond quickly and drive productivity improvements. Small models are typically cheaper and faster to run; large frontier models can demand many GPUs, while smaller Granite-class models can run on a single 32 GB GPU in some deployments.
Use case alignment: Different LLMs excel in different domains. Some are generalists that perform well across tasks; others are tailored or marketed for specific use cases. For example, an LLM might be excellent at summarization but mediocre at coding, or vice versa. It's wise to pilot your particular use case (whether that's customer service Q&A, generating technical documentation, performing legal research, or assisting in coding) and test which aligns best with the output style and accuracy you need.
To select the right LLM tool for your needs, follow these steps:
- Define your use case: Identify primary needs, such as customer support or data analysis, to narrow down options.
- Assess data requirements: Consider if you need real-time data (available on Grok and Perplexity, for example) or if enterprise-specific data (IBM watsonx) is crucial.
- Evaluate security needs: Ensure compliance with regulations. IBM watsonx and ChatGPT Enterprise offer robust security features.
- Consider integration capabilities: Check API availability and integration ease, with watsonx providing deep workflow embedding.
- Compare costs: Obtain pricing details, as costs vary (e.g. Grok pricing varies by model; for instance, grok-code-fast-1 lists $0.20/M input and $1.50/M output; Grok Live Search is billed per source). watsonx offers trial, essentials, and standard tiers.
- Trial and tested: If possible, use trial versions of LLMs to assess fit, ensuring alignment with enterprise needs.
Conclusion
Choosing the right LLM isn't about selecting the most powerful model or the one with the latest features. You need a model that best aligns with your business goals and specific use case, as well as your priorities in areas such as speed, reasoning ability, security, compliance, data transparency, and compatibility with your existing infrastructure. Carefully evaluate models against these factors, keeping in mind the long-term direction for AI in your business. A structured approach like this is more likely to help you maximise the lasting enterprise value from an LLM.
This blog was originally published on the IBM Community.