Modern LLM Architectures
As of mid‑2025, the LLM landscape has consolidated around three dominant model families, each led by a major player:
| Family | Organization | Key Strengths | Flagship Models |
|---|---|---|---|
| GPT | OpenAI | Scale, general performance, multimodal speed, ecosystem | GPT‑3 → GPT‑4 → GPT‑4o → GPT‑4.5 |
| Claude | Anthropic | Safety, transparency, reasoning depth | Claude 1 → 2 → 3 → 3.5 → 3.7 |
| Gemini | Google DeepMind | Multimodal context, tool integration, robotic/agent use | Gemini 1 → 1.5 → 2.5 |
| Let's have a look at each one. |
The GPT family
OpenAI’s Generative Pre-trained Transformer (GPT) models have redefined natural language processing and AI usability since the release of GPT-2 in 2019. Over successive iterations, the GPT family has introduced new capabilities, scaled in size and sophistication, and pioneered multimodal integration, tool use, and real-time AI assistance. These models form the backbone of ChatGPT, one of the most widely used AI platforms in the world today.
The GPT series has been at the forefront of general-purpose AI, shaping how people work, learn, and create. While GPT-4 remains the gold standard for quality, GPT-3.5 Turbo powers much of the world's AI infrastructure, and GPT-4o opens the door to real-time, multimodal interaction for everyone.
Architecture and traits
All models share a decoder-only transformer foundation, although key architectural improvements and design shifts have marked each generation.
| Model | Architecture | Key Traits | Use Case Highlights |
|---|---|---|---|
| GPT-3.5 | Dense decoder-only transformer. 175B parameters. | Fast, affordable, good general-purpose performance; no multimodal or advanced reasoning | Chatbots, summarization, translation, basic apps |
| GPT-4 | Likely Mixture-of-Experts (MoE) 1.75 TRILLION parameters (hypothetical) | Trillion-scale model, strong reasoning, coding, and instruction following; top benchmark scores | Complex reasoning, coding, research, premium apps |
| GPT-4 Turbo | Optimized GPT-4 variant (details undisclosed) | Nearly same capability as GPT-4, but faster and cheaper to run | ChatGPT Plus, production APIs, scale deployment |
The GPT-4 family (including GPT-4, GPT-4 Turbo, and GPT-3.5 Turbo) is not open-source. OpenAI has not publicly disclosed:
- The exact parameter count
- The training data sources
- The precise architecture (e.g., confirmation of Mixture-of-Experts, layer counts, tokenizers, etc.)
OpenAI did publish a full technical paper on GPT-3 in 2020: Language Models are Few-Shot Learners (Brown et al., 2020)
Training
Training data
| Model | What We Know (or Don’t) |
|---|---|
| GPT‑3 | Trained on ~300 billion tokens from public internet data — Common Crawl, WebText, books, Wikipedia, etc. Not open-source, but the paper listed categories. |
| GPT‑4 | Unknown. OpenAI has not disclosed sources, size, or preprocessing. Likely includes more curated, filtered, and possibly proprietary data (e.g., licensing deals with publishers or social platforms). |
| 🧠 Insight: The trend has shifted from “massive web scrape” (GPT-3) to curated, diverse, high-quality datasets (GPT-4+), including code, math, dialogues, images, and potentially structured documents. |
Training methodology
| Model | Method | Notes |
|---|---|---|
| GPT‑3 | Next-token prediction (causal LM) over a massive unsupervised corpus | Standard autoregressive transformer; purely self-supervised pretraining |
| GPT‑4 | Presumed same base (next-token prediction), but with enhancements: • Possibly Mixture-of-Experts (MoE) • Reinforcement Learning from Human Feedback (RLHF) • Fine-tuning on custom datasets | OpenAI has not confirmed this, but it's consistent with performance and hints in public statements. |
| GPT‑4 Turbo | Unknown. Same general approach, likely with infrastructure optimizations (e.g., distillation, quantization, MoE routing improvements) | Possibly trained using custom hardware/software stack for efficiency |
- With RLHF, the model is fine-tuned using rankings/preferences from humans to make it more helpful, safe, and aligned.
- Even if GPT-4 has not released training methodology, all known autoregressive LLMs, even in 2025, still rely on next-token prediction as the base pretraining objective — it's fundamental to how transformers learn. OpenAI’s own API docs and system behavior imply this. The models predict the next token given a context, which is literally what next-token prediction is. Features like logprobs, top-k sampling, and greedy decoding all stem from a next-token likelihood model.
Multimodal Capabilities
| Model | Modalities Supported | Details |
|---|---|---|
| GPT‑3 | Text only | No native support for images or audio |
| GPT‑4 | **Text + Images | In its multimodal variant, GPT‑4 can process images (e.g., charts, screenshots, diagrams). This version powers tools like Be My Eyes. |
| GPT‑4 Turbo / GPT‑4o | Text + Images + Audio | GPT‑4o adds real-time voice and audio processing, full multimodal interaction (vision, speech, code) |
| 🧠 Insight: GPT-3 was strictly text-only, but GPT-4 marks the start of OpenAI’s serious move into multimodality — though the base GPT-4 may still be text-only unless otherwise activated. |