A recommendation page from Artificial Analysis that suggests AI models for a given use case, drawing on the site's benchmark data across intelligence, speed, and cost. Free to use.
Why I recommend it: A quick way to turn "we need a model for X" into a shortlist with cost and speed tradeoffs attached. Useful in the first meeting; verify with your own tests before committing.
A running log of new model evaluations, benchmark methodology updates, and platform changes at Artificial Analysis, including Intelligence Index version changes and newly benchmarked models. Free to read.
Why I recommend it: The fastest way to see which models were benchmarked in the last week and when the scoring methodology changed. Bookmark it if you track the model landscape.
A platform from Artificial Analysis for building custom benchmarks from your own files, agent traces, or coding environment, then running them across leading models to compare quality, cost per task, and time per task. Benchmarks can be graded against objective rubrics or pairwise judging. Optima is a commercial product; the public announcement and product overview are free to read.
Why I recommend it: Standard benchmarks tell you which model is best in general; they cannot tell you which is best for your workload. If you are choosing a model for a real product, a custom benchmark on your own tasks is the right move — this is one way to do it without building the harness yourself.
A leaderboard comparing more than 250 AI language models across intelligence, price, output speed, latency, and context window, with per-model provider analysis. Free to read.
Why I recommend it: The single table to open when someone claims one model is "the best." Sort by cost per task or speed and the answer often changes — a useful reality check in vendor conversations.
