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Transformers are neural-network architectures that use attention to build context-aware representations of sequence elements. The original Transformer, introduced in 2017, combined an encoder and a decoder; many language models instead use decoder-only designs to generate text one token at a time. ChatGPT, Claude, and Gemini are product families, not architecture labels, and public disclosures do not establish that they all use the same design.
What a Transformer does
A Transformer processes a sequence—such as text represented as tokens—by relating the elements to one another and transforming their representations. Its defining mechanism, attention, lets the model compute which other positions are relevant when forming a representation for a given position. This is a mathematical operation, not human attention or evidence of understanding.
In 2017, Ashish Vaswani and coauthors introduced the Transformer in “Attention Is All You Need”. Their proposal replaced recurrence and convolution in the sequence-transduction architecture with attention mechanisms. The paper’s abstract describes it as “a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.” That describes the original proposal; it should not be read as a complete description of every later Transformer model.
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The original architecture has two major parts. The encoder builds representations of the input sequence. The decoder creates the output sequence while consulting the encoder’s representation. As Google Research’s 2017 explainer puts it, “A decoder then generates the output sentence word by word while consulting the representation generated by the encoder.”
1. Turn text into tokens and vectors
Text is divided into tokens, which may represent whole words, word pieces, or other units. The model maps each token to a numerical vector so it can be processed by the network.
2. Represent position and order
Attention alone does not tell the model the order in which tokens appeared. The Transformer therefore supplies positional information alongside token representations. The specific way a model represents position can vary.
3. Relate positions with self-attention
In self-attention, each position’s representation is computed using information from other positions in the same sequence. This lets the representation of a token reflect context, rather than only that token in isolation. Multi-head attention applies multiple learned attention transformations, enabling the network to combine different patterns of relationships.
4. Transform representations through stacked blocks
Attention is one part of the architecture. Stacked feed-forward layers further transform the representations, while residual connections and normalization components help organize information flow through the network. The original design uses attention and feed-forward components in repeated layers.
5. Generate output without looking ahead
When producing a sequence, the decoder uses a mask so a position cannot use future target tokens. During training, many target positions can still be processed in parallel because the mask blocks access to the future. During inference, output is generated autoregressively: the model predicts a next token, appends or otherwise incorporates it into the context, and predicts again.
How decoder-only models generate text
A decoder-only language model uses preceding context to score possible next tokens and then continues from a selected token. It does not include the original architecture’s separate input encoder and output decoder, so it is related to the Transformer but is not the complete encoder-decoder diagram often shown in introductory explanations.
OpenAI’s general explanation says that, as a model processes and learns from large volumes of text, it gets better at “recognizing patterns and predicting the most likely next word.” This is a high-level explanation, not a specification for every current model’s architecture. Predicting a likely continuation is not a database lookup and does not guarantee that the answer is factual.
Encoder-only, encoder-decoder, and decoder-only designs
| Design | Typical context direction | What it does |
|---|---|---|
| Encoder-only | Can use context from both directions in its input, depending on the model’s training setup. | Builds representations of an input sequence; it is not inherently a next-token generator. |
| Encoder-decoder | The encoder represents the input; the decoder generates output under a causal mask and consults the encoded input. | Maps an input sequence to an output sequence, as in the original Transformer design. |
| Decoder-only | Uses causal context: each position predicts from preceding context rather than future tokens. | Generates a sequence incrementally by predicting next tokens. |
These are architectural categories, not names for product brands. Models can also differ in attention patterns, positional methods, context handling, training, and other implementation choices.
What public disclosures say about Gemini, Claude, and ChatGPT
Statements about architecture should be tied to a named model and its documentation date. A product family can include models with different designs, and a disclosure about one model does not establish the internals of every model offered under the same brand.
Gemini
Google DeepMind’s Gemini 1.0 technical report describes the Gemini 1.0 family as decoder-only Transformers and discusses multi-query attention, a 32K context in that report, and multimodal training. Those are disclosures about Gemini 1.0, not claims that every later Gemini release has the same details. Google provides versioned model documentation; consult the card for the specific release when making a current-model claim.
ChatGPT and OpenAI models
ChatGPT is a product, and its name alone does not identify a single architecture. OpenAI’s 2025 announcement about gpt-oss describes those open-weight models as Transformers with mixture-of-experts, alternating dense and locally banded sparse attention, grouped multi-query attention, RoPE, and stated context lengths. These model-specific details do not establish that proprietary models available through ChatGPT share the exact gpt-oss design.
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Anthropic publishes Claude system cards covering capabilities, safety evaluations, and deployment decisions. Those materials do not establish the architecture of current Claude models, so assigning Claude a particular Transformer variant based on the available public documentation would go beyond what is confirmed.
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What the original Transformer results show—and do not show
The 2017 paper reported these results on machine-translation benchmarks:
- 28.4 BLEU on WMT 2014 English-to-German — Vaswani et al., 2017.
- 41.0 BLEU on WMT 2014 English-to-French — Vaswani et al., 2017.
- The English-to-French result was reported after 3.5 days of training on eight GPUs — Vaswani et al., 2017.
These are historical translation experiments, not scores for ChatGPT, Claude, or Gemini, and they do not show that Transformers outperform every alternative on every task. Google Research’s publication page says the proposed model was more parallelizable and faster to train than the recurrent and convolutional approaches compared in that work.
Why the famous diagram is not a blueprint for every AI assistant
The encoder-decoder diagram explains the original sequence-to-sequence proposal, not a universal architecture for modern assistants. Many language models use decoder-only designs, and models also vary in whether they use bidirectional or causal context, how attention is applied, and how they handle context. For specific products, rely on documentation for the named model and release. A general assistant label—or a broad explanation of next-token prediction—does not reveal all of a model’s internal design.
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