What is an LLMs? Large language models (LLM) are AI systems trained on great amounts of text data. They generate human-like language and are able to perform a variety of language related tasks, such as answering questions, translating language, writing code, summarizing texts and even generating works of creative writing like poetry and fiction. LLMs are the engines behind popular AI systems, with the capacity of understanding and generating text in knowledgeable and clear ways. The world of LLMs has become more competitive and some would say there is a race to be the best and the race includes popular well known companies and others that are lesser known. It is a global race. The Top LLMs Here are the top 10 best performing LLMs as of 2025. The breakthroughs and progress is moving very fast and it is difficult to keep track of which entity and LLM works best but luckily we have rankings that are updated often to help us navigate the space. Let’s take a closer look at the current top LLMs, they’re capabilities, whether they’re open or closed-sources, and how you can use them.: Chatbot Arena LLM Leaderboard: Community-driven Evaluation for Best LLM and AI chatbots How are the LLMs ranked? The LLM leaderboard from “Chatbot Arena” hosted by LMSYS and accessible through Hugging Face. The Elo score is calculated by pitting the models against one another using a prompt and seeing how each tool responds, then there is a vote on which LLM responds better. Like the Elo score, Massive Multitask Language Understanding (MMLU) which tests the AI on a variety of academic topics, scoring the model on its depth of knowledge. Ranking is also done by Multi-turn Benchmarks (MT-Bench), that measures how well a model handles complex, multi-step conversations, the higher the score the more the LLM has contextual awareness. What is the criteria for ranking? Some basic criteria for the rank is how consistent and safe the model is. The safety relating to how often a model produces safe and reliable responses. Along with this, community feedback is taken into concentration with the ranks of all the LLMs. Open vs. Closed LLM: Which is best for you? An open-source LLM whose model, structure, code and at times even training data are freely available to users. Anyone can download the model on their PC, modify it, or run it to their liking (depending on licensing terms). On the other hand, closed-source LLMs do not have their inner workings available to the public, often being developed by private companies that restrict access in some ways— usually offering the model through websites or application programming interface (API). Both types of LLMs have their own advantages and disadvantages. For open sources, with examples like DeepSeek, the ability to inspect, modify, and improve the model provides freedom and variety to its uses. However, the quality of the model depends on how well it was trained. For closed-sources like GPT-4, you can often expect high quality performance being generally safer to use, but often cost money to use at scale. Open LLM (Local/Private) vs Closed LLM (Web/Cloud Access Only): Open LLM (Local/Private): You can download it and run it on your own machine (like a laptop, server, or phone).
You don't need an internet connection after setting it up.
You have full control over it: you can modify it, fine-tune it, and it keeps your data private. You can download it and run it on your own machine (like a laptop, server, or phone). You don't need an internet connection after setting it up. You have full control over it: you can modify it, fine-tune it, and it keeps your data private. Examples: Llama 3 (if downloaded), Mistral, GPT4All, Ollama models. Examples: Llama 3 (if downloaded), Mistral, GPT4All, Ollama models. Closed LLM (Web/Cloud Access Only): You cannot download the actual model.
You access it by sending requests over the internet to a company’s servers.
You are dependent on them for access, updates, and uptime.
Your data passes through their servers, so privacy depends on their policies. You cannot download the actual model. You access it by sending requests over the internet to a company’s servers. You are dependent on them for access, updates, and uptime. Your data passes through their servers, so privacy depends on their policies. Examples: ChatGPT (official OpenAI site), Gemini, Claude on Anthropic's website. Examples: ChatGPT (official OpenAI site), Gemini, Claude on Anthropic's website. Which is the best for you? Why Keep It Private? Legal Reasons: HIPAA (healthcare), GDPR (Europe privacy law), or company policies require full control over data.
Trade Secrets: A company training AI on secret sauce formulas, designs, or patents.
Political/Military Use: High-stakes data that cannot risk leaks to outside servers.
Personal Security: Journalists, activists, or whistleblowers who can't afford surveillance risks.
Trust Issues: Some just don't trust big tech companies with their intellectual property. Legal Reasons: HIPAA (healthcare), GDPR (Europe privacy law), or company policies require full control over data. Legal Reasons Trade Secrets: A company training AI on secret sauce formulas, designs, or patents. Trade Secrets Political/Military Use: High-stakes data that cannot risk leaks to outside servers. Political/Military Use cannot Personal Security: Journalists, activists, or whistleblowers who can't afford surveillance risks. Personal Security Trust Issues: Some just don't trust big tech companies with their intellectual property. Trust Issues What are LLMs able to do? All models in the LLM leaderboard are capable of a vast spectrum of natural language tasks, including the following: Text generation: Storytelling, content creation, script writing Text generation: Storytelling, content creation, script writing Information retrieval: Q&A, summarization, fact-checking Information retrieval: Q&A, summarization, fact-checking Code generation: Python, JavaScript, and other languages Code generation: Python, JavaScript, and other languages Language translation: Multilingual support varies per model Language translation: Multilingual support varies per model Chatbot: Context-aware conversations and assistance Chatbot: Context-aware conversations and assistance Data analysis: Interpreting spreadsheets, summarizing trends, generating insights Data analysis: Interpreting spreadsheets, summarizing trends, generating insights Email and document drafting: Professional communication, reports, memos Email and document drafting: Professional communication, reports, memos Marketing: Ad copy, slogans, social media captions Marketing: Ad copy, slogans, social media captions Simulation: Roleplaying behavior, testing dialogue, scenario planning Simulation: Roleplaying behavior, testing dialogue, scenario planning Tutoring and education: Explaining concepts, solving problems, mock quizzes Tutoring and education: Explaining concepts, solving problems, mock quizzes Models like GPT-4o or other higher ranked models tend to be more stronger at maintaining long conversations, providing reasoning, and comprehending complex instructions. Open models are more customizable, giving a wider range to what can be done with these tools. For examples of some uses for LLMs, there are a handful of AI companions that act as therapists or assistants, where Gemini and GPT have been able to help with general mental health support by typing into a chat interface. How to interact with a LLM and how to try them out Working with an LLM can be as easy as signing into a website or as hands-on as running a model right on your computer. Closed-source models like ChatGPT, Gemini, or xAI offer instant access, just create an account, open a chat window, and start prompting. They handle everything in the cloud, making them ideal for casual users or those new to AI. But if you want more control, open-source models like DeepSeek can be downloaded from platforms like Hugging Face and run locally using tools like LM Studio, or Text Generation WebUI. To get started, you simply select a model from the Hugging Face model hub, download the files and load them into one of these desktop tools. Once installed, you can interact with the model in a local chat interface—no internet required after setup. This setup allows for full offline use, privacy, and customization, making it perfect for private environments, experimental workflows, or advanced users wanting to fine-tune the model for specific tasks. How are LLMs built? A simplified way to think about how LLMs are built starts with data collection. A massive dataset, often tens of terabytes—is gathered from public sources like Wikipedia, books, and a broad scan of the internet. This diverse pool of data is then cleaned and processed to remove low-quality content and filter out sensitive information. The text is tokenized into smaller units using techniques like byte-pair encoding, making it suitable for model training. From here, the real heavy lifting begins. The data is fed into a neural network. Specifically, a Transformer architecture, which uses self-attention mechanisms to understand language patterns and relationships across text. Training a model like Llama 2 can require about 6,000 GPUs running continuously for 12 days, costing around $2 million. This pre-training stage compresses language knowledge into the model’s datapool, much like zipping the internet into a file. Once pre-trained, the model can be fine-tuned on specific tasks or data to improve its accuracy for particular cases, like customer service or research. Models are then evaluated using benchmarks like MMLU and MT-Bench to assess performance. Finally, the trained model is deployed either through APIs and web-based chat interfaces, as with closed-source models. or by distributing downloadable model weights for local use, with open-source models. Over time, these models can continue to improve through user feedback and additional tweaking. Play How can LLMs go wrong? LLMs are powerful but aren't incapable of failure—or manipulation. They can make factual mistakes, known as hallucinations, like citing non-existent academic journals or fabricating historical quotes to support a response. These issues often stem from the model’s reliance on “fast thinking”, a pattern-based inference that works well for surface level tasks but can falter when deeper logic is needed. In more serious cases, models can be hacked or tricked into bypassing their safety protocols, this attack type is commonly known as jailbreak. For example, a user can role-play as a “grandma” telling a bedtime story and ask the model to include bomb-making instructions as part of the story, successfully tricking it into generating restricted content. Researchers have also identified universal suffixes for specific phrases that can reliably break safety rules across various prompts. Another vulnerability is prompt injection, where hidden commands are embedded in web pages or documents. When an LLM summarizes or interacts with that content, it may unknowingly follow those hidden instructions, potentially leaking private information. In real-world attacks, like one targeting Google Bard, prompt injection was used to uncover sensitive data. Lastly, during training, data poisoning or backdoor attacks can plant trigger phrases into the model’s memory. When those triggers are later used, the model behaves in a malicious, pre-programmed way. These vulnerabilities show that even sophisticated LLMs require careful monitoring, transparent safeguards, and responsible usage to ensure they behave as intended.

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