Every major LLM, side by side
Choosing a model means trading off cost, context window, speed and capability. This matrix puts the leading models from OpenAI, Anthropic, Google, Meta and Mistral in one place so you can compare them on the axes that actually drive your decision — and filter to just the ones that fit.
How it works
The table is a curated dataset of current flagship and workhorse models. Each row lists the context window, input and output price per million tokens, relative speed, whether it supports vision (image input), and a short note on what the model is best at. Use the provider filter to focus on one vendor, the sort control to rank by cheapest, largest context or fastest, and the vision toggle to hide text-only models. All filtering happens in your browser.
How to read it
- Cost vs capability: the cheapest models (GPT-4o mini, Gemini 1.5 Flash) handle the majority of everyday tasks; reserve premium reasoning models (o1, Claude 3 Opus) for genuinely hard problems.
- Context window: if you are feeding whole documents or codebases, Gemini 1.5 Pro’s 2M-token window or Claude’s 200K window matter more than raw quality.
- Speed: “Fast” models suit interactive chat and high-volume pipelines; “Slow” reasoning models trade latency for harder problem-solving.
Treat the prices as a planning estimate. For an exact monthly figure based on your own token volume, pair this with the LLM Pricing Calculator.