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In this post, I will examine the the available Large Language Models (LLM) in 2025.

What is LLM?

An LLM, or Large Language Model, is like a super-smart computer brain that can read and write just like a person!

Step 1: Understanding the Basics

Imagine you have a giant library filled with millions of books. An LLM has read all those books and learned how words work together. It knows how to answer questions, tell stories, and even help with homework!

Step 2: How Does It Work?

Just like when you learn to talk by listening to others, an LLM learns by reading lots of text from books, websites, and conversations. It remembers patterns in the language so it can guess what words should come next in a sentence. For example, if you say “Once upon a time,” it might continue with “there was a brave knight.”

Step 3: What Can It Do?

LLMs are very helpful! Here are some things they can do:

• Answer Questions: If you ask, “Why is the sky blue?” it will explain that sunlight scatters in the air.
• Tell Jokes: You could ask it for a joke, and it might say, “Why did the computer go to therapy? Because it had too many tabs open!”
• Help with Homework: If you’re stuck on math or science, an LLM can explain concepts step by step.
• Write Stories: You can start a story, and it will help you finish it.

Step 4: Why Is It Important?

Learning about LLMs is important because they are becoming part of our everyday lives. They help us communicate better and make learning more fun! Kids can even create their own simple versions of these models using tools designed for them.

The Technical Aspects of LLM

1. User Input

• User Interface (UI): This is where users interact with the LLM, providing input queries or prompts.
• Input Processing: Converts user input into a format suitable for the model, such as text normalization or tokenization.

2. Embedding Layer

• Embedding Model: Transforms input text into vector embeddings that capture semantic meaning. This can involve models like OpenAI’s text-embedding-ada-002 or others available from Hugging Face.

3. Vector Database

• Storage and Retrieval: A vector database stores embeddings and allows for efficient retrieval of relevant information based on user queries using techniques like Approximate Nearest Neighbors (ANN).

4. Transformer Architecture

• Transformer Layers: The core of the LLM consists of multiple transformer layers that process the embeddings through self-attention mechanisms, allowing the model to understand context and relationships between words.
  • Self-Attention Mechanism: Enables the model to weigh the importance of different words in a sentence relative to each other.
  • Feedforward Neural Networks: Each transformer layer typically includes feedforward networks that further process the attention outputs.

5. Output Generation

• Decoding Layer: After processing through transformer layers, this component generates predictions for the next word in a sequence based on learned probabilities.
• Post-processing: Converts model outputs back into human-readable text, applying any necessary formatting or adjustments.

6. Feedback Loop

• Reinforcement Learning from Human Feedback (RLHF): This optional component allows for continuous improvement of the model by incorporating user feedback on generated outputs, refining its responses over time.

Propertiary LLM vs Open-Source LLM

Understanding Proprietary LLMs

Proprietary LLMs are advanced AI models developed and maintained by private companies. These models are characterized by several key features:

1. Controlled Access: Proprietary LLMs, such as OpenAI’s GPT-4o and Anthropic’s Claude 3.5, are not publicly available for modification or redistribution. Users must adhere to strict licensing agreements that dictate how the model can be used.
2. Performance and Support: These models often deliver state-of-the-art performance due to significant investments in research and development by their creators. They come with robust support systems, including comprehensive documentation and customer service, making them easier to integrate into applications.
3. Cost Implications: Accessing proprietary LLMs typically involves subscription fees or pay-per-use pricing models, which can be expensive for smaller organizations or individual developers. For example, API usage can range from $0.01 to $0.10 per 1K tokens, with enterprise solutions costing significantly more.
4. Security and Privacy: Proprietary models usually have managed security solutions that help protect sensitive data during processing. However, users may need to share their data with the provider, raising privacy concerns.
5. Limited Customization: While these models offer high performance out of the box, they often lack flexibility for customization compared to open-source alternatives.

Understanding Open-Source LLMs

Open-source LLMs, on the other hand, are publicly accessible AI models that allow users to inspect, modify, and distribute the code freely. Their characteristics include:

1. Transparency and Community Collaboration: Open-source models like Meta’s LLama 3 and Mistral benefit from community-driven development where researchers and developers contribute improvements and innovations collaboratively.
2. Cost-Effectiveness: Many open-source LLMs are free to use, making them attractive for organizations with limited budgets. However, there may still be infrastructure costs associated with running these models locally or on cloud services.
3. Customization Options: Users can fine-tune open-source models for specific tasks or domains without restrictions imposed by a single provider. This flexibility allows organizations to adapt the model closely to their unique requirements.
4. Potential Performance Gaps: While open-source LLMs are rapidly improving and closing the performance gap with proprietary counterparts, they may still lag behind in certain benchmarks or capabilities.
5. Self-Management of Security: Organizations using open-source LLMs must manage their own security protocols since they have full control over the deployment environment.

Available Large Language Models [ Updated April 2025]

Proprietary Large Language Models

1. GPT-4.5
   • Developed by OpenAI, GPT-4.5 is the latest iteration in the GPT series, focusing on advancing unsupervised learning capabilities. It maintains multimodal functionalities, allowing it to process both text and images effectively.
2. Claude 3.5 Sonnet
   • Created by Anthropic, Claude emphasizes constitutional AI principles to ensure outputs are helpful and accurate. It has enhanced programming capabilities and can interact with computers like a human.
3. Gemini 2.0 Flash
   • This model from Google replaces its predecessor Bard and offers multimodal capabilities across various applications, including text, audio, and video processing.
4. Ernie 4.0
   • Baidu’s Ernie powers its chatbot with significant user engagement in Mandarin and other languages, boasting an impressive parameter count rumored to be around 10 trillion.

Open Source Large Language Models

1. LLaMA 3
   • Developed by Meta, LLaMA 3 includes models optimized for dialogue applications with sizes ranging from 8 billion to 70 billion parameters.
2. Gemma 2
   • Another offering from Google DeepMind, Gemma 2 supports high-speed operations across various hardware platforms with parameter sizes of 9 billion and 27 billion.
3. Mistral-8x22B
   • This model utilizes a sparse Mixture-of-Experts architecture with strong multilingual capabilities and excels in mathematics and coding tasks.
4. DeepSeek-R1
   • An open-source model focused on reasoning tasks that employs reinforcement learning techniques for complex problem-solving.
5. Qwen Series (Qwen2.5-Max)
   • Developed by Alibaba Cloud, this series includes models designed for mathematical reasoning and coding tasks while being efficient in resource usage.
6. Vicuna-13B
   • A fine-tuned version of the LLaMA model that performs well in conversational contexts and has been evaluated against leading proprietary models like ChatGPT.
7. BLOOM
   • A collaborative effort aimed at democratizing access to LLMs across multiple languages with a parameter size of up to 176 billion.
8. GPT-NeoX-20B
   • Developed by EleutherAI, this autoregressive model is designed for advanced language understanding tasks while being freely accessible under an open-source license.

What are the top performing LLMs 2025?

To explore the LLM leaderboard and find the LLM that suits your projects, I recommend exploring the following resources:

https://artificialanalysis.ai/leaderboards/models

https://www.vellum.ai/llm-leaderboard

https://llm-stats.com/