Transformer-based architectures in ChatGPT, Claude, and Gemini

The internal mechanics that power AI chatbots across OpenAI, Anthropic, and Google models rely on distinct adaptations of the transformer architecture.

The transformer remains the foundational architecture behind every major AI chatbot in use today. However, how it is implemented, optimized, scaled, and extended varies considerably between OpenAI’s ChatGPT (GPT-4o, GPT-5), Anthropic’s Claude models (Sonnet and Opus), and Google’s Gemini series (2.5 Flash and Pro). This article explores the deep technical mechanics behind these transformer-based systems and explains how their differences affect reasoning, latency, multimodal input, and real-world usage.

All major AI chatbots are built on transformer architecture.

Attention mechanisms, self-supervised learning, and layer stacking form the structural base of every leading chatbot today.

The transformer architecture, first introduced in the 2017 paper Attention Is All You Need, underpins all major large language models (LLMs). The core innovation of transformers is the self-attention mechanism, which enables the model to weigh the relevance of every token in an input sequence to every other token — regardless of their position.

This architecture is highly parallelizable, unlike RNNs or LSTMs, and is scalable across billions of parameters. The most advanced chatbots today — ChatGPT, Claude, and Gemini — all extend this baseline with proprietary improvements.

Key components of a generic transformer architecture include:

Component	Function in Transformer
Self-Attention	Allows each token to attend to others and build contextual meaning.
Feed-Forward Networks	Projects attention outputs into higher-dimensional latent spaces.
Layer Normalization	Stabilizes and accelerates training.
Positional Encoding	Injects sequence order into non-recurrent architecture.
Residual Connections	Helps preserve information across deep layers.

While the blueprint is shared, how each vendor adapts the transformer can differ radically — especially in terms of token length, memory optimization, sparsity, multimodal fusion, and latency handling.

OpenAI's ChatGPT uses refined dense transformer models with agentic control.

From GPT-3 to GPT-4o and GPT-5, OpenAI has gradually layered reasoning, memory, and multimodality into a unified transformer system.

GPT-4o, which powers most current ChatGPT experiences (free and Plus), uses a dense transformer backbone optimized for low latency and multimodal input (text, vision, and audio). The model integrates:

• Dense attention layers across long contexts (up to 128,000 tokens in GPT-4o; up to 256,000 in GPT-5).

• Sparse token routing only in selected prototype versions (GPT-4 was rumored to use MoE variants).

• Native multimodal layers, not bolted on post-hoc, enabling fluid image+text parsing.

GPT-5 expands on this by introducing agentic control modules, enabling the model to plan, evaluate and execute subtasks — all embedded within transformer-block logic.

Model	Context Length	Multimodal	Architectural Notes
GPT-3.5	16,384 tokens	No	Classic dense transformer
GPT-4	32,000 (rumored)	Partial	May use sparse-MoE, mix of dense/sparse
GPT-4o	128,000 tokens	Yes	Fully dense, fast streaming, multimodal core
GPT-5	256,000 tokens	Yes	Adds planning layers and orchestration logic

Notably, GPT-5 and GPT-4o leverage custom inference hardware (e.g., Triton stack + Azure AI Infra) to execute their dense transformers in real-time across modalities.

Claude builds on Constitutional AI layered over a scaled dense transformer.

Anthropic’s Claude models optimize for safety and reasoning depth using training-stage innovations rather than major structural divergence.

The Claude Sonnet and Opus models are dense transformers trained on very large corpora using Anthropic's unique Constitutional AI framework — a method for embedding behavioral alignment directly into the training loop. Architecturally, Claude resembles GPT-4 in many ways, but with some notable differences:

• Massive internal latent width allows better in-context reasoning.

• Self-reflective optimization loops help reduce hallucinations and bias during generation.

• Claude 3.5 and Opus support extremely long context windows (up to 200,000–300,000 tokens).

• Rumors point to block-wise recurrence mechanisms to simulate short-term memory inside transformers.

While Claude does not (yet) natively support audio/video input, it is highly optimized for dense text reasoning, legal/technical documents, and long-form comprehension.

Claude Model	Context Length	Multimodal	Design Emphasis
Claude 2	100,000 tokens	No	Long context, high precision
Claude 3 Sonnet	200,000 tokens	Yes (text+image)	Safety-guided responses, lower latency
Claude 3 Opus	200,000+ tokens	Yes	Deep logic, reflection, context tracking
Claude 4.1 Opus	~300,000 (rumored)	Yes	Internal memory simulation, RAG synergy

Internally, Claude may be using a non-MoE transformer, but tuned with alignment prefilters and debate-style evaluators at scale.

Gemini introduces mixture-of-experts and joint vision-language transformers.

Google’s Gemini models push for hybrid architectures using both dense and sparse MoE transformers with live grounding capabilities.

Unlike GPT and Claude, Gemini 1.5 and 2.5 introduce large-scale Mixture-of-Experts (MoE) layers — meaning only a subset of transformer blocks are active per token. This creates a more efficient transformer that dynamically routes tokens to different "experts" based on their content.

Additionally, Gemini incorporates joint vision-language transformers, allowing it to process image and text within a shared latent space — rather than fusing them late in the pipeline. This design supports:

• Real-time vision recognition (tables, charts, diagrams)

• Document parsing and layout awareness

• Fast contextual grounding via Google Search

Gemini Model	MoE Support	Multimodal	Architectural Feature
Gemini 1.5 Pro	Yes	Text, Image	Sparse attention with joint latent space
Gemini 2.5 Flash	Partial	Optimized for latency	Fast routing with limited expert activation
Gemini 2.5 Pro	Yes	Text, Image, Audio	Full MoE transformer + grounded generation

This combination of sparse MoE, long context (up to 1 million tokens in Gemini 1.5 Pro), and retrieval grounding gives Gemini a unique transformer variant that emphasizes scalability, energy efficiency, and precision in fact-based tasks.

Comparison of transformer adaptations across leading AI chatbots.

Feature	ChatGPT (GPT-4o / 5)	Claude (3.5 / 4.1)	Gemini (2.5 Pro)
Base Transformer Type	Dense	Dense with latent expansion	Sparse MoE + joint VL
Max Context Window	256,000 tokens (GPT-5)	~300,000 tokens	Up to 1,000,000 tokens
Multimodal Capability	Text, Image, Audio (GPT-4o)	Text, Image	Text, Image, Audio
Memory Simulation	Basic (token buffer)	Reflective windowing	Retrieval + cache
Streaming & Latency	Extremely fast (GPT-4o)	Moderate to fast	Flash model optimized
Grounding & Search	Limited (via tool use)	No native grounding	Native Google grounding
Alignment Strategy	RLHF + system messages	Constitutional AI	Prompt tuning + safety filters
Token Routing	Dense (all layers activated)	Dense	Sparse (experts selected)

Architectural choices shape chatbot behavior and strengths.

Performance, latency, logic, and alignment all stem from how each vendor adapts the transformer to its priorities.

• OpenAI favors dense, real-time multimodal transformers with unified layers and increasingly agentic behavior.

• Anthropic prefers monolithic models focused on ethical alignment and precision, with architectural tuning focused on context and reflection.

• Google builds sparse MoE transformers with native grounding, optimized for scale, factuality, and enterprise integration.

Each of these paths reflects deeper architectural decisions: how to trade off speed vs reasoning, cost vs accuracy, context vs control — and those choices shape what each chatbot can and cannot do.

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