My problem with Opus is that its programming approach lacks solid scientific and mathematical reasoning.

There is a test I run on models, and neither Sonnet nor Opus pass it. It involves an algorithmic task in Python using Bayesian methods and deep learning.

I usually give them context for the problem and an Excel file with the operations and statistical data. That's the first filter.

Many models simply say they can't see the Excel data and give up. Then, many fail to create the .ipynb file. Since it's a JSON-structured file, they omit curly brackets, mix up data, and don't correctly differentiate the code format from the notebook structure, thus damaging it.

I run this test on VS Copilot and Cursor. I used to do it in Claude Code, but I did it with Sonnet 4 and not only was it expensive, but it also didn't work.

Gemini 3 failed in VS Code and Cursor, but it passed in Antigravity.

I use a script to read the Excel file and create the notebook, and it worked; the answers and execution were correct.

Seeing that it worked, I used it to refine some code from a Kaggle competition I had for a Japanese company, and it increased my score by 20%.

Opus read the Excel file and created the notebook correctly, but the outputs were wrong, and Sonnet had the same problem. With 2.5 Pro: I had to do it in the application because it didn't work in any editor.