Choosing the right LLM

For most organisations exploring AI, the conversation starts with ChatGPT. And while GPT-4 remains a strong generalist, it’s no longer the only option you have and increasingly, it’s not always the best tool for the job. With the rise of alternative large language models (LLMs) like Anthropic’s Claude, Google’s Gemini, Meta’s Llama, and open-source contenders like Mistral and Mixtral, business leaders now face a new kind of decision: which model best fits our real-world use cases?

The truth is, each LLM has strengths and blind spots. Some are better at long context reasoning. Others outperform on code. Some shine when security and self-hosting are non-negotiable. For example, Claude excels at summarising large volumes of information due to its long context window, while GPT-4 Turbo tends to be strongest for general-purpose tasks and custom workflows. Meanwhile, open-source models like Mistral offer more control and customisability, making them appealing for businesses with in-house AI teams or sensitive data.

What’s emerging is a shift from “Which model is best?” to “Which model is best for this?” A financial services firm handling confidential client records might lean towards a self-hosted model with tight access controls. A product team building a support chatbot may prefer a fast, fine-tuned GPT-4 deployment. A marketing agency running high-volume content production might benefit from Claude’s natural tone and formatting capabilities.

The bottom line? The best LLM for your organisation isn’t a fixed choice, it’s a strategic one. Understanding how your teams actually use AI, whether for analysis, drafting, research, customer service, or automation should shape your model stack. As LLMs become embedded into workflows, choosing the right model mix becomes just as important as the tools you build on top of them.