Your comprehensive guide to understanding AI models and their capabilities
An AI model is a program that has been trained on data to recognize patterns and make decisions. Think of it as a highly specialized tool that has learned from examples to perform specific tasks.
Modern AI models use neural networks inspired by the human brain, with layers of interconnected nodes that process information and learn from experience.
Training: Models learn from vast amounts of data, adjusting their internal parameters to recognize patterns.
Inference: Once trained, models can process new inputs and generate predictions or outputs based on what they learned.
Fine-tuning: Models can be further specialized for specific tasks with additional focused training.
Specialized in understanding and generating human language. These models can write, summarize, translate, and answer questions.
Best for: Text generation, chatbots, code writing, content creation, translation
Examples: GPT-4, Claude, Gemini
View LLM Leaderboard →Process and understand images and visual content. They can classify, detect objects, generate images, and analyze visual data.
Best for: Image classification, object detection, image generation, visual analysis
Examples: DALL-E, Stable Diffusion, CLIP
View Vision Leaderboard →Work with sound and audio data. They can transcribe speech, generate music, synthesize voices, and analyze audio content.
Best for: Speech recognition, text-to-speech, music generation, audio analysis
Examples: Whisper, ElevenLabs, MusicGen
View Audio Leaderboard →Process and generate video content. They can create videos from text, analyze video content, and perform video-to-video transformations.
Best for: Video generation, video analysis, video editing, action recognition
Examples: Sora, Runway Gen-2, Make-A-Video
View Video Leaderboard →The number of trainable values in the model. More parameters generally mean more capability but also higher computational costs. Measured in millions (M) or billions (B).
The maximum amount of text a model can process at once. Longer context allows the model to maintain coherence over longer conversations or documents. Measured in tokens (roughly 3-4 characters per token).
The amount of data used to train the model. More diverse and high-quality training data typically leads to better performance. Measured in tokens or gigabytes.
The time it takes for a model to process input and generate output. Lower latency means faster responses, which is crucial for real-time applications.
Benchmarks that measure how well a model performs on specific tasks. Different benchmarks test different capabilities (reasoning, math, coding, etc.). Higher scores indicate better performance.
Selecting the right AI model depends on your specific use case, budget, and requirements. Consider these factors:
What do you need the model to do?
Balance capability with practical constraints:
Consider costs and infrastructure:
Use our comparison tool to evaluate models side-by-side based on their specifications and capabilities.
Try the Comparison Tool →Browse our comprehensive leaderboards to find the best models for each category.
View All Leaderboards →Click on any model in our leaderboards to see detailed specifications, capabilities, and use cases.
Browse Models →The AI field evolves rapidly. Check back regularly for updates on new models and capabilities.
Start comparing models and find the perfect one for your needs.