AI Models
Compare AI models side by side
Choose up to three models. You will see their relative tiers, strengths and limits next to each other. Share the link to show the same comparison to your team.
Choose models to compare
Pick at least one model to see its capability tiers. Pick two or three to compare.
Tiers are relative and reviewed by hand, not benchmark scores. Check each provider for current pricing and limits.
Before you decide
A comparison is a shortlist, not an answer
The tiers here are relative and based on provider documentation and our own use. They help you narrow the field. The final choice should come from a test on your own examples: the same prompts, the same documents and the same success measure for each model.
When you run that test, record quality, format errors, response time and cost per thousand requests. Also check data terms, available regions and rate limits. If two models score about the same, pick the cheaper or faster one and keep the other as a fallback.
We run this kind of test as part of AI model evaluation. You can also start from the model picker or browse by category, such as general purpose, reasoning, vision and embeddings.
Keep exploring
Related on ExecMedia
Related services
View all related services- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- AI model evaluation Test candidate AI models on your real data and tasks, then pick the one that balances quality, speed and cost.
- LLM integration Add a large language model to software you already have, with the guardrails, costs and logging handled.
Guides and articles
View all guides and articlesAI models
View all ai models- Open weights Models whose weights you can download and run on your own servers or a cloud of your choice.
- Gemma 4 Google's open-weight model family under Apache 2.0, in sizes from phone-friendly to 31B, with image input and function calling.
- Llama 4 Scout An open-weight Llama 4 model with image input and a very long context window, for teams that want to host a capable model themselves.
- Llama 4 Maverick The larger Llama 4 open-weight model, with image input and a 1 million token context window, for self-hosted assistants and analysis.
- Llama 3.3 70B An older, text-only open-weight Llama model with tool use and a 128K token window, still widely hosted and well understood.
- Meta Muse Spark Meta's newer proprietary model family, offered through the Meta Model API, with text, image, video and PDF input, tool calling and a 1 million token window.
Glossary terms
View all glossary terms- Context window A context window is the maximum amount of text, measured in tokens, that an AI model can consider at once, including the question, documents and its answer.
- Fine-tuning Fine-tuning is further training an existing AI model on your own examples so it learns a specific style, format or task.
- Inference Inference is the step where a trained AI model is used to produce an output, such as an answer, a label or a prediction, from new input.
- Large language model A large language model, or LLM, is an AI model trained on vast amounts of text that can understand and generate language, and often images and code.
- LLM integration LLM integration is connecting a large language model to your software and data, so AI features work inside your own products and processes.
- Open-weights model An open-weights model is an AI model whose trained parameters are published, so anyone can download and run it on their own hardware under its license.