GPT: models, generations and how they work.

What do the letters GPT mean?

Generative means the model can produce output, such as an answer, summary or piece of code. It generates that output from learned patterns and the context of the request, rather than merely selecting a complete prewritten response. This makes varied tasks possible, but it also allows the system to formulate errors that never appeared literally in a source.

Pre-trained refers to training that took place before your request. The model has already learned many patterns. Processing a conversation is usually use of that trained model, not complete retraining. Further training can improve instruction following and other behaviour, so the term does not imply that development ends after the first training stage.

Transformer refers to the architectural tradition behind GPT. Transformers use attention mechanisms to process relationships in their inputs. This is a useful technical starting point, but it does not disclose the entire implementation of a current commercial model. The name alone cannot establish a parameter count or complete internal design.

How does GPT relate to an LLM?

A large language model, or LLM, is a broad category of language models. GPT is a particular family in the wider generative-model landscape. Other organisations also develop LLMs. Not every language model is GPT, and not every AI application uses an OpenAI model. Avoid using GPT as a generic brand-free name for all chatbots.

Many modern models process images as well as text. Describing them only as language models may therefore understate their possible uses. It remains useful to distinguish language processing, visual input and tools. A model may analyse an image without producing pixels itself; an application can call a separate image generator when needed.

For a user, the specific combination matters most. A capable assistant without access to a PDF cannot check its contents. Conversely, a straightforward task such as organising clearly labelled fields may gain little from a more demanding model. Choose capabilities according to the work rather than assuming every task benefits equally from a larger option.

Training and using a model are different

Training changes model parameters. During use, also called inference, an already trained model processes a request. Instructions and supplied information influence the response, but do not automatically change all model weights permanently. Uploading a file does not mean you have trained your own GPT generation.

Consider a new price list. You provide it and ask for three product descriptions. The system can use those prices in the current task. It does not thereby acquire permanent, universal knowledge of all future changes. A later task needs current information again through context, memory or a data connection.

With RAG, an application retrieves relevant information and supplies it to the model. That helps ground an answer in sources. Retrieval and training solve different problems. To establish which products are available today, access to current inventory matters more than general exposure to product descriptions during training.

The original ChatGPT announcement described additional training with human feedback. This is a historical example of shaping behaviour, not a full account of every current GPT model. Specific claims about training should come from the technical publications for the particular model. Do not fill unpublished details with assumptions based on an older generation.

Generations and lasting family names

A generation label and family name communicate different things. In GPT-6.1 Sol, 6.1 identifies the version and Sol helps describe its place in the offering. The same family name can recur across generations. That is why this dictionary keeps a stable family page rather than creating a new article for every version number.

The position checked on 7 October 2026 includes GPT-6 Luna, GPT-6.1 Sol and GPT-6 Astra in the current API catalogue. GPT-5.6 Terra remains documented and available during the rollout according to user documentation. These are not four models from an identical generation. That matters particularly when comparing capabilities or pricing.

A model can appear in the API, Work and Codex at different times. ChatGPT's ordinary Chat experience may have a different selection. Record the full model name, product and date in an evaluation. Saying that a task was tested with GPT is not enough to interpret the outcome later.

Comparing Luna, Terra, Sol and Astra

Model selection versus reasoning effort

Selecting the model and setting reasoning effort are separate decisions. Reasoning effort controls the processing allowance of a supported model; it does not turn Sol into Astra. Higher settings may require more time and usage. They do not guarantee that a conclusion will be correct.

Compare a straightforward translation with an investigation of conflicting documents. Limited processing may be enough for the translation. Extra reasoning may help compare conditions and exceptions in the investigation. However, if a necessary document is missing, more reasoning cannot provide it. The system must request information or limit the conclusion to the evidence available.

Interface labels do not always correspond exactly to API values. Settings are also not directly comparable across generations. Current user documentation describes Ultra as a workflow using subagents, rather than a separate GPT family. Check what your product actually provides and how its usage is measured.

For a fair comparison, keep the source material and acceptance criteria stable. Otherwise, a better answer could reflect a clearer instruction rather than a different model or effort level. Change one important factor at a time when you need to understand why a workflow improves or deteriorates.

Context, tokens and current information

Models process text as tokens. A context window limits how much information a model can use in a processing step. It is not an unlimited personal memory. A large capacity also does not guarantee flawless retrieval or application of every detail in a long dossier.

Provide the central question, authoritative documents and relevant exceptions. A hundred files without a purpose are less useful than a clear question supported by a source map. Ask for the evidence behind important conclusions. Also check whether the model has actually compared the documents or merely summarised them separately.

A knowledge cutoff is not a release date or a guarantee of complete knowledge through that date. Changing facts still need recent sources. A model can retrieve information using tools only when those tools are available and used. A confident answer without evidence is not proof that a search took place.

Multimodality and tools

With tool use, a model can request an action for the surrounding application to execute. The tool might calculate, search, run code or retrieve data. Its result becomes further input. This creates a workflow in which language processing and observable operations complement each other.

For example, a script can count duplicate product codes while a model explains which categories need attention. A suitable visual model can help transcribe readable labels. Unclear characters and missing fields should remain visible. A useful workflow therefore includes an outcome that requires further checking instead of forcing every case into a confident answer.

This combination explains why two applications using the same model can perform differently. One may have current documents, a calculation environment and clear output rules; another may receive only a brief question. Evaluate the full process. GPT supplies an important component, while sources, tools and human judgment determine whether the result is fit for use.