Table of Contents
Large Language Models (LLMs) are the driving force behind the AI revolution, powering tools like ChatGPT, Google Bard, and Claude. But not all LLMs are built the same. Each model has its own design, purpose, and strengths. In this article, you’ll get a quick, clear comparison of 7 top LLMs you should know about in 2025.
What Are LLMs?
Large Language Models are artificial intelligence systems trained on massive volumes of text to understand, generate, and reason in human language. These models learn language patterns, grammar, facts, and reasoning abilities by analyzing billions of words during training.
From answering your questions to generating stories and solving code, LLMs are transforming everything—from education to enterprise tech.
1. BERT – Google’s Language Understanding Breakthrough
BERT (Bidirectional Encoder Representations from Transformers) is unique because it reads text in both directions at once. It excels at understanding the context of words in a sentence.
- Use Cases: Sentiment analysis, question answering, next-sentence prediction
- Architecture: Encoder-only
- Strength: Understanding sentence-level context
- Open-source: Yes
- Ideal For: Natural language understanding tasks like search engine improvements
2. GPT – The Pioneer of Generative AI
GPT (Generative Pre-trained Transformer) by OpenAI sparked the modern generative AI era. It’s decoder-only and trained to predict the next word in a sentence, enabling powerful text generation capabilities.
- Latest Version: GPT-4o (2024), which supports multimodal input (text + images)
- Use Cases: Creative writing, coding, chatbots, multi-turn conversations
- Architecture: Decoder-only
- Strength: Fluent, human-like generation
- Open-source: No (proprietary)
- Ideal For: AI writing assistants, conversational agents
3. LLaMA – Meta’s Open-Source Powerhouse
LLaMA (Large Language Model Meta AI) is Meta’s series of open-source models with sizes ranging from 7B to 65B+ parameters. Despite smaller sizes, LLaMA 3 and 4 perform like GPT-4, thanks to innovations like SwiGLU activations and Rotary Positional Embeddings.
- Use Cases: Research, on-device AI, community training
- Architecture: Decoder-only
- Strength: Efficiency and accessibility
- Open-source: Yes
- Ideal For: Developers, researchers, and enthusiasts
4. PaLM – Google’s Multilingual Master
PaLM (Pathways Language Model) is designed for extreme efficiency and multilingual capability. It uses multi-query attention to reduce memory usage and supports advanced tasks like few-shot learning and logic-based reasoning.
- Version: PaLM 2 powers Bard and Google Workspace AI
- Use Cases: Translation, code generation, logical tasks
- Architecture: Decoder-only
- Strength: Scalable and multilingual
- Open-source: No
- Ideal For: Enterprises and multilingual platforms
5. Gemini – Google DeepMind’s Multimodal Future
Gemini is Google’s next-gen LLM that handles text, images, audio, video, and code—within a single model. It uses a Mixture-of-Experts (MoE) design, which activates only the parts of the model needed for a task, saving compute.
- Current Version: Gemini 2.5 (2025), supports up to 1 million tokens of context
- Use Cases: Long-form reasoning, multimodal tasks, enterprise AI
- Architecture: MoE + Multimodal
- Strength: Multimodal intelligence and tool integrations
- Open-source: No
- Ideal For: Advanced AI applications and automation
6. Mistral – Open-Source Meets Efficiency
Mistral, developed in France, is known for compact yet powerful models. Its Mixtral 8x7B uses a sparse MoE design, allowing fast processing without sacrificing quality. The newer Mistral Medium 3 (2025) rivals models like Claude at a lower cost.
- Use Cases: Coding, enterprise-level reasoning
- Architecture: Sparse Mixture-of-Experts
- Strength: Performance with speed
- Open-source: Partially
- Ideal For: Businesses seeking affordable, high-performance AI
7. DeepSeek – China’s Logic-Oriented Challenger
DeepSeek, a spin-off from High-Flyer AI, features sparse MoE Transformer models. With a total of 670B parameters but only 37B active per task, it achieves incredible compute efficiency. It’s optimized for multilingual logic and real-world deployments.
- Use Cases: Logical reasoning, large-scale applications
- Architecture: Sparse MoE
- Strength: Low compute cost, strong reasoning
- Open-source: Yes
- Ideal For: Global deployment and logic-heavy AI tasks
Quick Comparison Table
Model | Architecture | Specialty | Open/Closed |
---|---|---|---|
BERT | Encoder-only | Sentence understanding | Open |
GPT | Decoder-only | Fluent generation | Closed |
LLaMA | Decoder-only | Open, efficient models | Open |
PaLM | Decoder-only | Coding, multilingual | Closed |
Gemini | MoE + Multi | Multimodal + long context | Closed |
Mistral | Sparse MoE | Logic, enterprise-ready | Mixed |
DeepSeek | Sparse MoE | Reasoning, low compute use | Open |
Final Thoughts
As AI becomes a part of everyday life, knowing the differences between these LLMs helps you choose the right tool for your needs—whether you’re building apps, deploying chatbots, or just exploring AI’s capabilities.
For developers and tech enthusiasts, open-source models like LLaMA, BERT, and DeepSeek provide customization and transparency. For enterprises, proprietary models like GPT, Gemini, and PaLM offer cutting-edge capabilities with robust support.
Also Read: NVIDIA AI Aerial: Improves Connectivity with Emerging Tech