JeliaWhat it is
GPT is OpenAI's family of large language models, available through ChatGPT for people and through an API for software. It is the most widely used set of models, with a broad range of sizes and capabilities and a large ecosystem built around it.
On this estate Claude is the primary model family. GPT is the deliberate alternative: kept current, kept in reach, and used where it is the better fit.
Why keep a second vendor
- No single point of dependence. Prices change, terms change, models are retired and services have outages. A business that can only work with one provider has handed that provider a great deal of power over it.
- Different models are good at different things. Models differ in strengths, speed and cost, and the ranking moves every few months. Choosing per job beats assuming one vendor wins every job.
- A second opinion. For work where a mistake is costly, asking a different model to check the answer catches errors that the same model would make twice.
- Staying honest about the tools. Using more than one keeps judgement grounded in what each actually does now, rather than what it did when it was first chosen.
Where it fits, and where it does not
GPT is a strong, general-purpose choice, and for many organisations it is the obvious default — especially where staff already use ChatGPT and the business tools around it. I would not talk anyone out of it on principle.
Where it does not fit is the same place no model fits: work that needs accountability, work where nobody will check the output, and anything involving data that should not leave your control. Swapping one vendor for another does not change those rules. The other limit is practical. Keeping two vendors means two sets of terms, two bills and two integrations to maintain, so it should be done on purpose, not by accident.
How the choice is made
The question is never "which vendor do we like?" It is "what does this job need?" — the quality of answer, how fast, how often, at what cost, and with what data. Some jobs need the strongest reasoning available. Many are routine and should go to something smaller and cheaper. Once the job is described honestly, the choice of model is usually straightforward, and it is revisited when the models change.
What I would do for a client
Design the integration so the model is a choice, not a foundation. Put your AI use behind your own service, so the application asks for "a summary" or "a classification" and that service decides which provider answers. Account for cost per job across every provider. Keep a short, written record of which model does which job and why. When a better or cheaper option appears, switching should be a configuration change and a test run, not a rebuild.
Questions people ask
Should we standardise on one AI vendor to keep things simple?
Standardise on one by default, by all means. Just build so that leaving, or adding a second, is cheap. Simplicity and lock-in are not the same thing.
Is GPT better or worse than Claude?
It depends on the job, and the answer changes as new models arrive. Testing both on your actual work tells you more than any published benchmark.
Does running two vendors double the cost?
Not if each job goes to one model. You pay for what you use. The real extra cost is maintaining two integrations, which is why it is worth putting both behind one service.