I am thus making my own benchmarks: https://aibenchy.com
I could modify the tests to emphasize the requirements, but then, what's the point of a test. In real life, we expect the AI to do something if we ask it, especially for agentic use-case or in n8n, because if the output is slightly wrong, the entire workflow fails.
I haven't done my own tests, but I did notice a lot of models are very low there. You'll give them specific instructions and they'll ignore them and just pattern match to whatever was the format they saw most commonly during training.
And it outputs:
**RIGHT**
With markdown bold formatting... This is probably fine in a chat app, but when you use this in a workflow, it will break the workflow if you then have an if check like if(response === 'RIGHT')...
I just started on this, so currently adding more tests and I keep improving the UI. Let me know if you have any suggestions.
The ranking currently is mostly about the "smartest" model, which is most likely to respond correctly to any given question or request, regardless of the domain.
If the question was: "I want to go to a cafe 50m away. Should I walk or drive?" I would hope that all of my friends would answer quite a bit more pointed than the LLMs: "Walk you lazy son of a ..., why are you even asking?".
Considering that, I'd say that most LLMs are being quite nice.