Yet Another Applied LLM Benchmark is an evaluation suite by security researcher Nicholas Carlini, built from questions he had actually asked language models over the previous year. Instead of trivia or synthetic puzzles, every test is derived from a real task โ which makes the benchmark a refreshingly honest probe of what models can and cannot do for a working practitioner. The repository has picked up over 1,000 stars on GitHub.
The benchmark’s tasks span the messy reality of applied LLM use: converting a Python function into an equivalent but faster C function, decompiling Python bytecode back to readable source, explaining the functionality of minified JavaScript, identifying an obscure encoding format such as uuencoding in raw data, and similar jobs drawn from genuine day-to-day work. Because the tests come from real usage rather than a committee’s idea of difficulty, they expose failure modes that leaderboard benchmarks often miss. Carlini also writes up observations from running the suite, which makes the repository useful reading even before you run a single test. The grading harnesses let you run the suite against different models and compare results consistently.
One reason the benchmark resonates is its honesty about difficulty: because the questions were collected from real interactions rather than designed to be answered, even strong models fail a meaningful share of them, and the gaps between models are informative rather than compressed near the ceiling. The write-ups accompanying the results discuss specific model behaviors on specific tasks, which is often more useful for choosing a model for real work than a single aggregate score reported on a leaderboard.
The benchmark is free and open source under the GPL-3.0 license; you only supply API access or local compute for the models you want to evaluate.
It is aimed at researchers and power users who are skeptical of headline benchmark scores and want a grounded, task-based read on real model capability.