How to choose a large language model for your business

Large language models now sit behind customer support, document review, reporting and writing tools in many businesses. Choosing one has become an ordinary business decision, but it is harder than it looks: the options change every few months, and the comparisons you find online are often out of date by the time you read them.

That’s why this guide doesn’t rank models. It sets out what to compare, how to test, and how to avoid being locked in. For background on how these models developed, see our piece on the evolution of large language models.

What “enterprise-ready” should mean

A model that is fine for personal use isn’t automatically suitable for a business. When people say enterprise-ready they usually mean:

• written terms on how your data is handled, stored and used
• security controls such as single sign-on, access management and audit logs
• reliable service and support, with clear pricing
• the ability to connect to your own documents and systems
• a published policy on how long each model version is supported

These are things to check in the contract, not to assume from a brand name.

The main providers

The established names include OpenAI (the GPT models), Anthropic (Claude), Google (Gemini), Meta (the open Llama models), Mistral and Cohere, and several others worth watching. Each releases new versions regularly, and their relative strengths shift as they do.

Some patterns have held for a while:

• Google’s models fit naturally if your team works in Google’s office tools, with strong handling of images and long documents.
• OpenAI’s models have the widest range of tools and integrations and the largest user base.
• Anthropic’s models are often chosen for careful writing, long documents and coding, and for their safety focus.
• Open models such as Llama and Mistral’s open releases can run on your own servers or a private cloud, which suits strict data requirements, though you carry the technical work.
• Cohere focuses on business uses such as search over company documents and extracting information.

Treat those as starting points for your shortlist, not conclusions. A published ranking tells you how a model did on someone else’s tests. What matters is how it does on yours.

What to compare

The task. A model that writes well may be weaker at extracting figures from invoices or following a long set of rules. Define the job first: customer replies, summarising contracts, answering staff questions from your policies, drafting reports, coding help.

Accuracy on your own material. Run your own examples through each candidate. Generic benchmarks don’t reflect your documents, your terminology or your customers.

Data handling. Where is data processed, how long is it kept, is it used for training, and who at the provider can see it? If you handle sensitive personal, financial or health information, this can decide the choice before quality does.

Cost. Pricing is usually based on how much text goes in and out. A task that looks cheap in a trial can become expensive at a few hundred thousand requests. Estimate the cost per completed task, and consider using a smaller, cheaper model for simple jobs and a larger one for hard ones.

Speed. A reply that takes twenty seconds is fine for a report and hopeless for a live chat.

Integration. How easily does it connect to your CRM, service desk, document store and security tools? A slightly weaker model that fits your systems often beats a stronger one that doesn’t.

Support and lifecycle. Providers retire older model versions on a schedule. Check how long you will have to move, and how the provider communicates changes.

How to test

You don’t need a lab, only a tidy process.

1. Collect 30 to 50 real examples of the task, including some awkward ones: unclear emails, messy documents, edge cases.
2. Agree what a good answer looks like before you see any results. Write it down.
3. Run two or three candidates on the same set, using the same instructions.
4. Have the people who do the work score the results, ideally without knowing which model produced which answer.
5. Record cost and speed alongside quality.
6. Pick the one that does the job well enough at an acceptable cost, not the one with the best headline score.

Keep that test set. When a new version arrives, or a provider retires one, you can rerun it in an afternoon.

Plan to change your mind

Models improve quickly and prices move. A system built tightly around one model can be expensive to change. A few habits help:

• keep your instructions and your connections to company data separate from the model itself
• avoid features that only one provider offers unless they are essential
• keep your test set up to date
• review your choice every six months

If you’d like help working out which tasks to test first, or turning the winner into something your team uses every day, that’s part of our AI implementation and automation work. You can also start with the AI Readiness Index to see where your data and systems stand.