Definer Wiki

Appearance

AI Models ​

Large Language Model (LLM) is the engine that generates responses when you interact with AI. Choosing the right model is crucial, as different models have distinct strengths and weaknesses that make them better suited for specific tasks.

This article provides a brief overview of current state-of-the-art AI models to give you a starting point for your research. Understanding each model's capabilities will help you select the best one for your specific needs.

State-of-the-Art Models ​

Here's a selection of popular models available today, divided into two categories: cloud-based models that require an internet connection and API key, and local models you can run offline on your own hardware.

Cloud Models ​

To use these models, you'll need to sign up for an API key from the provider. They require an internet connection and typically charge based on usage.

Popular cloud models include:

  • gpt-4.1-mini - smaller, more affordable version of GPT-4.1 with great performance for everyday tasks (OpenAI)
  • claude-sonnet-3.7 - balanced AI assistant with strong natural conversation skills (Anthropic)
  • gemini-2.5-pro - advanced AI model that excels at complex reasoning and strong performance across text, code, and multimodal content (Google)
  • grok-3-beta - conversational AI model with a distinctive personality and approach to answering questions, designed to be both helpful and engaging (xAI's)

Local Models ​

These models run directly on your hardware through platforms like Ollama or LM Studio. While they may not match the capabilities of the largest cloud models, they offer privacy, no usage fees, and work offline.

Popular local models include:

  • gemma3:12b - medium-sized AI language model by Google that speaks multiple languages while being small enough to run on more affordable computers
  • llama3.2:3b - small instruction-tuned text only model by Meta that is optimized for multilingual dialogue use cases
  • qwen2.5:7b - multilingual LLM by Alibaba Cloud with particular strength in Chinese and English language processing
  • deepseek-r1:14b - distilled DeepSeek-R1 model based on Qwen (thinking model)
  • deepseek-r1:8b - distilled DeepSeek-R1 model based on Llama (thinking model)

How to Research and Compare Models ​

There are dedicated leaderboards where you can compare model performance for specific AI tasks. These benchmarks provide objective measurements to help you identify the best model for your needs.

Visit lmarena.ai to see up-to-date comparisons across various tasks. The AI landscape evolves rapidly – a model that tops the charts today might be outperformed tomorrow!

Additional benchmarking resources can be found in this awesome-ml repository.

Learn More ​

Cloud Model Resources ​

  • OpenAI Models - Documentation for GPT models, including capabilities, limitations, and usage guidelines
  • Anthropic's Models - Comprehensive information about Claude models and their responsible AI approach
  • Google AI Models - Details on Gemini and other Google AI offerings
  • xAI Models - Information about Grok and other xAI developments

Local Model Resources ​

  • Ollama Library - Browse and download models optimized for local deployment through Ollama
  • LM Studio Models - A curated collection of models compatible with the LM Studio interface

Model Aggregators and Marketplaces ​

  • OpenRouter Models - A service that provides unified access to multiple AI models through a single API
  • Hugging Face Model Hub - The largest repository of open-source models with thousands of options for various tasks

Open-Source Model Collections ​