7 Popular Large Language Models Explained in 7 Minutes

7 Popular LLMs Explained in 7 Minutes

This article provides a concise overview of seven prominent Large Language Models (LLMs), explaining their core architectures and key features in a way that can be grasped in approximately seven minutes. The goal is to offer a quick yet informative introduction to these powerful AI tools.

Introduction to LLMs

Large Language Models (LLMs) are integral to many daily tasks, trained on vast datasets to understand and generate human-like language. While their core function is similar, their underlying architectures vary significantly, impacting their capabilities. For instance, DeepSeek excels in reasoning, Claude in coding, and ChatGPT in creative writing.

1. BERT (Bidirectional Encoder Representations from Transformers)

• Developer: Google (2018)
• Architecture: Transformer encoder-only.
• Key Innovation: Deep bidirectional attention, considering text in both directions simultaneously.
• Training: Masked language modeling and next-sentence prediction.
• Sizes: BERT Base (110M parameters), BERT Large (340M parameters).
• Special Tokens: [CLS] for sentence representation, [SEP] for sentence separation.
• Applications: Fine-tuned for sentiment analysis, question answering (SQuAD), etc.
• Significance: First model to truly understand the full meaning of sentences.

2. GPT (Generative Pre-trained Transformer)

• Developer: OpenAI (2018-present).
• Evolution: From GPT-1 to GPT-4 (2023) and GPT-4o (May 2024) with multimodal capabilities.
• Architecture: Decoder-only Transformer.
• Training Objective: Next-token prediction on massive text corpora.
• Usage: Fine-tuning for specific tasks or zero-/few-shot prompting.
• Key Paradigm: "Pre-train and prompt/fine-tune."
• Characteristics: Fluent text generation, few-shot learning abilities.
• Access: Primarily proprietary, accessed via APIs; exact architectures often undisclosed.

3. LLaMA (Large Language Model Meta AI)

• Developer: Meta AI (February 2023-present).
• Architecture: Open-source, decoder-only Transformer with architectural tweaks (SwiGLU, RoPE, RMSNorm).
• Sizes: Ranging from 7 billion to 70 billion parameters (LLaMA 1), with Llama 4 released in April 2025.
• Performance: Competitive with larger models (e.g., LLaMA 13B outperformed GPT-3 175B on many benchmarks).
• Key Novelty: Efficient training at scale combined with more open access to model weights (research-restricted).
• Impact: Spurred significant community use and development.

4. PaLM (Pathways Language Model)

• Developer: Google Research.
• Architecture: 540-billion parameter, decoder-only Transformer (part of Google's Pathways system).
• Training: 780 billion tokens across thousands of TPU v4 chips; uses multi-query attention.
• Key Feature: Strong few-shot learning capabilities due to vast, diverse training data (webpages, books, code, social media).
• PaLM 2 (May 2023): Improved multilingual, reasoning, and coding abilities; powers Google Bard and Workspace AI.

5. Gemini

• Developer: Google DeepMind & Google Research (late 2023-present).
• Architecture: Natively multimodal Transformer, supporting text, images, audio, video, and code.
• Key Features: Massive scale, long context support (e.g., 1 million tokens in Gemini 1.5 Pro), Mixture-of-Experts (MoE) for efficiency.
• Gemini 1.5 Pro: Uses sparsely activated expert layers.
• Gemini 2.5 Series (March 2025): Enhanced "thinking" capabilities.
• Gemini 2.5 Flash/Pro (June 2025): Stable releases; Flash-Lite previewed as cost-efficient and fast.
• Sizes: Ultra, Pro, Nano (scalable from cloud to mobile).
• Significance: Flexible, highly capable foundation model due to multimodal pretraining and MoE scaling.

6. Mistral

• Developer: Mistral AI (French startup, 2023-present).
• Mistral 7B (Sept 2023): 7.3B parameter Transformer, similar to GPT but optimized for inference (Grouped-Query Attention, Sliding-Window Attention).
• Performance: Outperformed Llama 2 13B with smaller size.
• Licensing: Mistral 7B released under Apache 2.0 license.
• Mixtral 8x7B: Sparse Mixture-of-Experts (MoE) model, competitive with GPT-3.5 and Llama 2 70B.
• Mistral Medium (May 2025): Proprietary, enterprise-focused model; competitive performance at lower cost.
• Mistral Magistral (June 2025): Focused on explicit reasoning; small version open-source, Medium enterprise-only.
• Note: Shift towards closed-source models for enterprise offerings.

7. DeepSeek

• Developer: DeepSeek (Chinese AI company, founded 2023).
• Architecture: Highly sparsely activated Mixture-of-Experts (MoE) Transformer.
• DeepSeek v3/R1: Hundreds of expert sub-networks per layer, only a few activated per token.
• Size: Over 670 billion total parameters, but only ~37 billion active per response.
• Benefits: Faster, cheaper to run than dense models of similar size; high capability at lower compute cost.
• Optimizations: SwiGLU, RoPE, FP8 precision.
• Licensing: Models released under open licenses.
• Performance: Rivals leading models like GPT-4 in multilingual generation and reasoning.

Conclusion

This overview highlights the diverse architectures and capabilities of leading LLMs, from BERT's bidirectional understanding to the multimodal and MoE advancements in Gemini and DeepSeek. Understanding these differences is key to leveraging the right model for specific AI tasks.