What is the best AI tool in 2026?

The best AI tool depends on your use case. For writing, Jasper and Smartli lead the market. For generative AI, ChatGPT, Claude and Gemini are top choices. For SEO, Surfer SEO and Semrush AI are most popular. AI Supermarket compares 107+ tools across 13 categories so you can find the best option for your needs.

How do I compare AI tools?

AI Supermarket lets you compare any AI tools side-by-side for free. Click the Compare button on up to 4 tools, then click the Compare bar to see a full feature and pricing comparison. You can also filter by category, price and user count.

What are the best free AI tools?

Top free AI tools include ChatGPT (free tier), Claude (free tier), Gemini (free tier), ClickUp (free for teams), Fireflies.ai (free meeting transcription) and Beehiiv (free newsletter up to 2,500 subscribers). AI Supermarket lists free trials and free tiers for all 107+ tools.

What is generative AI?

Generative AI refers to AI systems that can create new content including text, images, code and audio. Leading generative AI tools include ChatGPT by OpenAI, Claude by Anthropic, Gemini by Google, Grok by xAI and LLaMA by Meta. AI Supermarket has a dedicated Generative AI category comparing all major models.

Where can I find the latest AI news?

AI Supermarket has a built-in AI News feed that aggregates the latest AI news from Ars Technica, VentureBeat, Import AI, MIT Technology Review and more, updated every 2 hours.

What AI tools do professionals use?

Professionals commonly use Surfer SEO for content marketing, ElevenLabs for voice synthesis, Synthesia for video creation, ActiveCampaign for marketing automation, ClickUp for project management and AdCreative.ai for advertising. AI Supermarket compares all of these in detail.

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About AI Supermarket

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AI Supermarket is a free, independent directory of 107+ AI tools across 13 categories. We help individuals, teams and businesses find and compare the right AI software — without the noise, without the bias.

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AI Glossary

Plain-English definitions of the most important AI terms and concepts in 2026.

Artificial General Intelligence

AGI

AI that can perform any intellectual task a human can. Not yet achieved. Current systems are narrow AI.

AI Agent

An AI system that autonomously plans and executes multi-step tasks without human input at each step.

AI Hallucination

When an AI model confidently generates false information. A key reliability challenge for LLMs.

Application Programming Interface

API

How software applications talk to each other. Most AI tools offer APIs so developers can build AI features into their products.

AI Alignment

The challenge of ensuring AI systems behave in ways that match human intentions and values. A key safety research area.

Autonomous AI

AI systems that can make decisions and take actions independently without human supervision. Powers self-driving cars and AI agents.

Accuracy

A measure of how often an AI model makes correct predictions. Calculated as correct predictions divided by total predictions.

Activation Function

A mathematical function applied in neural networks that determines whether a neuron should fire. Examples include ReLU and Sigmoid.

Adversarial Attack

Inputs deliberately crafted to fool an AI model into making incorrect predictions, exposing vulnerabilities in AI systems.

AutoML

Automated Machine Learning. Tools that automate the process of selecting and tuning ML models, making AI more accessible to non-experts.

AI Bias

Bias

Systematic errors in AI outputs caused by flawed training data or model design, often leading to unfair or discriminatory results.

Backpropagation

The algorithm used to train neural networks by calculating how much each weight contributed to errors and adjusting them accordingly.

Benchmark

A standardised test used to evaluate and compare AI model performance. Examples include MMLU, HumanEval and ImageNet.

BERT

Bidirectional Encoder Representations from Transformers. Google's language model that reads text in both directions, improving understanding of context.

Big Data

Extremely large datasets that traditional tools cannot process. AI and machine learning are often used to extract insights from big data.

Chatbot

An AI-powered conversational interface. Modern chatbots use LLMs to understand context and hold natural conversations.

Context Window

The maximum text an AI model can process at once. Larger windows allow models to handle longer documents and conversations.

Chain-of-Thought

A prompting technique where AI is instructed to reason step by step before giving an answer, improving accuracy on complex tasks.

Claude

Anthropic's AI assistant family, known for safety, nuanced reasoning and long context windows. Competes with ChatGPT and Gemini.

Computer Vision

The field of AI that enables machines to interpret and understand visual information from images and video.

Convolutional Neural Network

CNN

A neural network architecture designed for processing grid-like data such as images, using filters to detect patterns and features.

Clustering

An unsupervised ML technique that groups similar data points together without pre-defined labels. Used in recommendation systems.

Deep Learning

A subset of machine learning using neural networks with many layers. Powers most modern AI breakthroughs in vision, language and audio.

Diffusion Model

The AI architecture behind image generators like Stable Diffusion and DALL-E. Learns to reverse a noise-adding process to create images.

Data Augmentation

Techniques to artificially expand training datasets by creating modified versions of existing data, improving model robustness.

DALL-E

OpenAI's text-to-image AI model that generates realistic images and art from natural language descriptions.

Decision Tree

A simple ML model that makes predictions by following a tree of if-then rules. Easy to interpret and explain.

Embedding

A numerical representation of text that captures meaning. Used to power semantic search and recommendation systems.

Emergent Behaviour

Unexpected capabilities that appear in large AI models that were not explicitly trained for. GPT-4 showed emergent reasoning abilities.

Explainable AI

AI systems designed to provide human-understandable explanations for their decisions. Important for regulated industries like healthcare.

Expert System

Early AI programs that encode human expertise as rules to solve specific problems. A precursor to modern ML systems.

Epoch

One complete pass through the entire training dataset during model training. Models are typically trained for multiple epochs.

Fine-tuning

Training a pre-trained model on your own dataset to specialise it for a specific task. Cheaper than training from scratch.

Few-shot Learning

Teaching an AI to perform a task by showing it only a handful of examples, rather than thousands of labelled training samples.

Foundation Model

A large AI model trained on broad data that can be adapted to many tasks. GPT-4, Claude and Gemini are foundation models.

Feature Engineering

The process of selecting and transforming raw data into meaningful inputs for ML models to improve their performance.

Generative AI

AI that creates new content including text, images, code, audio and video. Powers ChatGPT, Midjourney and Sora.

GPT

Generative Pre-trained Transformer. The architecture behind ChatGPT and many other leading language models. Developed by OpenAI.

GAN

Generative Adversarial Network. Two neural networks compete — a generator creates fake data, a discriminator tries to detect it. Used in deepfakes.

Gemini

Google DeepMind's flagship AI model family. Competes with GPT-4 and Claude, with native multimodal capabilities.

Gradient Descent

The optimisation algorithm used to train most AI models by iteratively adjusting weights to minimise prediction errors.

Hyperparameter

Settings that control how an AI model is trained, such as learning rate and batch size. Set before training begins, not learned from data.

Human-in-the-Loop

AI systems that incorporate human feedback during operation or training. Used to improve safety and accuracy of AI outputs.

Hugging Face

The leading open-source AI platform where developers share models, datasets and tools. Often called the GitHub of AI.

Inference

The process of using a trained AI model to make predictions on new data. Distinct from training, which is how models learn.

Image Recognition

AI's ability to identify objects, people, scenes and actions in images. Powers facial recognition, medical imaging and self-driving cars.

In-context Learning

The ability of LLMs to learn new tasks from examples provided in the prompt, without updating the model weights.

JSON

JavaScript Object Notation. The standard data format used by most AI APIs to send and receive structured data between applications.

Jupyter Notebook

An open-source tool widely used in AI development for writing and running Python code alongside explanations and visualisations.

Knowledge Graph

A structured representation of real-world entities and their relationships. Used by search engines and AI to understand factual context.

K-means Clustering

K-means

A popular unsupervised learning algorithm that groups data points into K clusters based on similarity. Used in customer segmentation.

Knowledge Base

A structured repository of information that AI systems can query to answer questions. Used in RAG systems and chatbots.

Large Language Model

LLM

A neural network trained on vast text. The technology behind ChatGPT, Claude and Gemini. Can write, code, reason and summarise.

LLaMA

Meta's open-source large language model family. Can be run locally on consumer hardware, enabling private AI deployments.

Latent Space

The compressed internal representation that AI models learn during training. Similar concepts cluster together in latent space.

Learning Rate

A hyperparameter that controls how quickly a model updates its weights during training. Too high causes instability, too low is slow.

Loss Function

A mathematical formula that measures how wrong an AI model's predictions are. Training aims to minimise the loss function.

Multimodal

AI that processes multiple input types such as text, images, audio and video. GPT-4o and Gemini are leading multimodal models.

Machine Learning

A branch of AI where systems learn patterns from data rather than following explicit rules. The foundation of modern AI.

Model

An AI system trained on data to make predictions or generate outputs. GPT-4, Stable Diffusion and Whisper are all AI models.

MCP

Model Context Protocol. Anthropic's open standard allowing AI models to connect with external tools, databases and services.

NLP

Natural Language Processing. The field of AI focused on enabling computers to understand and generate human language.

Neural Network

Computing systems inspired by the human brain, consisting of layers of interconnected nodes. The foundation of deep learning.

NLP Pipeline

A sequence of text processing steps in AI systems: tokenisation, embedding, encoding and generation, each building on the last.

Overfitting

When an AI model learns training data too well and fails to generalise to new data. A common cause of poor real-world AI performance.

OpenAI

The AI research company behind ChatGPT, GPT-4, DALL-E and Whisper. One of the most influential AI labs in the world.

On-premise AI

AI systems deployed on a company's own servers rather than in the cloud. Preferred for sensitive data where privacy is critical.

Prompt

The input or instruction you give to an AI model. Prompt engineering is the skill of crafting prompts to get the best AI outputs.

Prompt Engineering

The practice of designing effective inputs for AI models to produce better outputs. A key skill for working with LLMs.

Parameters

The learned weights inside an AI model. GPT-4 has an estimated 1.8 trillion parameters. More parameters generally means more capability.

Pre-training

The initial phase of training a large AI model on massive datasets before fine-tuning. Extremely compute-intensive and expensive.

Quantisation

Reducing the precision of model weights to make AI models smaller and faster, enabling deployment on consumer hardware.

Q-learning

A reinforcement learning algorithm where an AI learns to make decisions by estimating the value of actions in given states.

RAG

Retrieval-Augmented Generation. A technique that gives LLMs access to external knowledge to reduce hallucinations and improve accuracy.

Reinforcement Learning

Training AI through rewards and penalties. Used to teach game-playing AIs and to align LLMs with human preferences via RLHF.

RLHF

Reinforcement Learning from Human Feedback. The technique used to train ChatGPT and other assistants to follow instructions helpfully.

Responsible AI

The ethical development and deployment of AI systems that prioritise fairness, transparency, accountability and human wellbeing.

Random Forest

An ensemble ML method that builds many decision trees and combines their outputs. Reliable and resistant to overfitting.

SaaS

Software as a Service. Cloud-based software accessed via subscription. Most AI tools are delivered as SaaS products.

Stable Diffusion

An open-source text-to-image AI model. Unlike DALL-E, it can be run locally and fine-tuned for specific artistic styles.

Supervised Learning

ML training using labelled data where the correct answer is provided. The most common AI training approach.

Synthetic Data

Artificially generated data used to train AI models when real data is scarce, expensive or contains sensitive information.

Sentiment Analysis

AI that determines the emotional tone of text — positive, negative or neutral. Used in brand monitoring and customer feedback.

Token

The basic unit of text an LLM processes. Roughly 4 characters or 0.75 words. LLM pricing and context windows are measured in tokens.

Transformer

The neural network architecture that powers all modern LLMs. Introduced in the 2017 Google paper Attention Is All You Need.

Transfer Learning

Using a model trained on one task as a starting point for a different task. Makes AI training faster and cheaper.

Text-to-Speech

AI that converts written text into natural-sounding spoken audio. Powers voice assistants and accessibility tools.

Tokenisation

The process of splitting text into tokens before feeding it to an LLM. Different models use different tokenisation strategies.

Unsupervised Learning

ML training on data without labels, where the model finds patterns on its own. Used for clustering and anomaly detection.

Underfitting

When an AI model is too simple to capture the patterns in training data, leading to poor performance on both training and new data.

Vector Database

A database optimised for storing and searching embeddings. Powers semantic search and RAG systems. Examples: Pinecone, Weaviate.

Vision Transformer

A transformer architecture applied to image recognition tasks. Outperforms CNNs on many computer vision benchmarks.

Weights

The numerical parameters inside a neural network that are adjusted during training. A model's learned knowledge is stored in its weights.

Whisper

OpenAI's open-source speech recognition model. Can transcribe and translate audio in 99 languages with high accuracy.

XAI

Explainable Artificial Intelligence. Methods and techniques that make AI decision-making transparent and understandable to humans.

YOLO

You Only Look Once. A real-time object detection AI algorithm that processes entire images in a single pass, enabling fast video analysis.

Zero-shot Learning

Zero-shot

An AI model ability to perform a task without being given any examples, relying only on pre-trained knowledge.

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