LLM vs GPT
LLM and GPT often get used interchangeably in business meetings. They aren't the same — one is the category, the other is a specific architecture inside it.
The key difference: LLM is the general term for any large language model. GPT is one specific family of LLMs, developed by OpenAI.
| Dimension | LLM | GPT |
|---|---|---|
| Scope | Category — covers all large language models | A specific model family / architecture |
| Built by | Many — OpenAI, Anthropic, Google, Meta, Mistral | OpenAI |
| Examples | GPT, Claude, Gemini, Llama, Mistral | GPT-3.5, GPT-4, GPT-4o, GPT-5 |
| Architecture | Varies — most are transformer-based | Generative Pre-trained Transformer — decoder-only |
| Use in a sentence | "We're evaluating which LLM to deploy" | "We're using GPT-4o for this workflow" |
When to use LLM
Say "LLM" when you're talking about the category or comparing options across vendors.
When to use GPT
Say "GPT" when you specifically mean an OpenAI model.
FAQs
Is every GPT an LLM?
Yes — GPT is one specific kind of LLM. The reverse isn't true: Claude, Gemini and Llama are LLMs but not GPTs.
Why is the distinction important?
In vendor discussions and contracts, "GPT" implies OpenAI. Saying "we use GPT" when you actually mean Claude or Gemini is a fast way to lose technical credibility.
Are all LLMs based on transformers?
Almost all the current state-of-the-art ones are, though newer architectures (Mamba, state-space models) are starting to appear in research.