Architectural Differences in Large Language Models: ChatGPT, Gemini, and Claude
This article explores the core architectural and design decisions that differentiate leading large language models (LLMs) like ChatGPT, Gemini, and Claude. It delves into how choices in scaling (MoE vs. dense), multimodality (native vs. sequential), context handling, and alignment mechanisms lead to their distinct user-visible behaviors. Understanding these architectural forks is crucial for comprehending the underlying engineering trade-offs in building frontier AI models.
Read original on ByteByteGoThe article highlights that while all major LLMs like ChatGPT, Gemini, and Claude share a fundamental transformer-based generative neural network architecture, their differing user experiences stem directly from distinct architectural decisions made during development. These choices consistently influence model behavior across releases and provide a framework for understanding their strengths and weaknesses.
Key Architectural Divergences
- Density and Scaling: How models scale total capacity without proportionally increasing query cost.
- Multimodality: Approaches to handling non-text inputs like images, audio, and video.
- Context Management: How models handle long input sequences and maintain relevance.
- Alignment: Methods used to shape the base model into a helpful, safe assistant.
Density: Mixture of Experts (MoE) vs. Dense Models
One significant architectural fork is how to scale model capacity efficiently. Google's Gemini models widely adopt Mixture of Experts (MoE), where a router directs tokens to a subset of specialized 'experts' within the network. This allows for an enormous total parameter count with only a fraction activated per query, leading to greater knowledge density per compute dollar. The trade-off can be increased variance and load balancing challenges. OpenAI's GPT models, while details are scarce for GPT-4, describe a routing architecture for GPT-5 that selects between distinct sub-models, implying a move towards similar sparsity, while earlier versions were more dense, offering predictable per-token behavior but harder scaling.
MoE for Scalability
Mixture of Experts (MoE) is a powerful technique for scaling large neural networks by activating only a subset of parameters for each input. This can drastically reduce computational costs while still allowing for a massive total parameter count, making it ideal for extremely large models where cost efficiency is paramount.
Multimodality: Native vs. Sequential Approaches
The approach to handling diverse input types (images, video, audio) also differentiates LLMs. Google's Gemini adopted a native multimodal approach from inception, training a single network on all modalities simultaneously. This allows direct processing of video and audio within the same transformer layers. OpenAI initially used a sequential approach for GPT-4 (text-first with separate encoders for vision), but later transitioned to a unified architecture with GPT-4o. Anthropic's Claude has maintained a text-first focus with strong vision capabilities. These choices directly impact how seamlessly models handle different media types.
Context Window Management
The 'context window' refers to the amount of input a model can process in a single pass. Longer context windows allow reasoning over more data but face challenges with computational cost (quadratic scaling of attention) and quality degradation ('context rot'). Gemini and Claude have pushed for much larger context windows (up to 1 million or 10 million tokens), enabling them to process entire codebases or multi-hour transcripts. OpenAI has taken a more conservative stance on raw window size, focusing on efficient routing between sub-models instead, suggesting different trade-offs in handling large inputs.