GPT-4 and Gemini represent the frontier of large language models—massive multimodal systems capable of processing text, images, audio, and video while demonstrating near-human performance across a wide range of cognitive tasks.

Architecture Overview

While exact architectural details are proprietary, both models are believed to build on the Dense Transformer foundation with significant enhancements for multimodal processing and scale.

GPT-4 is reported to use a Mixture-of-Experts (MoE) architecture with approximately 1.8 trillion total parameters across ~16 experts, with roughly 110B active parameters per forward pass. Visual inputs are processed through a CLIP-like vision encoder and integrated via cross-attention or early fusion. The model uses a dense decoder-only Transformer backbone with MoE feed-forward layers.

Gemini takes a natively multimodal approach, trained from the ground up on interleaved text, image, audio, and video data rather than retrofitting vision onto a text model. Gemini Ultra is estimated at similar scale to GPT-4, using efficient attention mechanisms and potentially MoE components. It processes visual inputs at varying resolutions and can handle long video inputs.

Key Innovations

  • GPT-4's MoE scaling: Using sparse MoE allows a much larger total model while keeping inference cost manageable—only a fraction of experts activate per token
  • RLHF + Constitutional AI: Both models use extensive reinforcement learning from human feedback and safety training for alignment
  • Native multimodality (Gemini): Training on all modalities jointly from the start, rather than adding them post-hoc, enables tighter cross-modal reasoning
  • Long context: GPT-4 Turbo supports 128K tokens, Gemini 1.5 Pro supports up to 1M tokens using efficient attention mechanisms

Common Use Cases

General-purpose AI assistants, code generation and debugging, multimodal reasoning (analyzing images, charts, documents), creative writing, research assistance, education, professional analysis (legal, medical, financial), and as the backbone for specialized AI applications.

Notable Variants & Sizes

GPT-4 (~1.8T total, ~110B active), GPT-4 Turbo (128K context), GPT-4o (optimized for speed), GPT-4o mini. Gemini Ultra (largest), Gemini Pro (balanced), Gemini Nano (on-device, 1.8B/3.25B), Gemini 1.5 Pro (1M context), Gemini 1.5 Flash (efficient).

Technical Details

GPT-4 (reported): ~96 layers, 128 heads, MoE with 16 experts and top-2 routing (~110B active of 1.8T total). Trained on ~13T tokens. 128K context via ALiBi or RoPE extensions. Gemini: uses SentencePiece tokenizer, efficient attention (likely a form of multi-query or grouped-query attention), processes images as variable-length token sequences. Both models use multi-stage training: pretraining on web data, supervised fine-tuning on demonstrations, and RLHF/RLAIF alignment. Inference uses speculative decoding and KV-cache optimizations for efficiency.