To be quite honest, I’ve found very little marginal value in using reasoning models for coding. Tool usage, sure, but I almost never use “reasoning” beyond that.
Also, LLMs still cannot do basic math. They can solve math exams, sure, but you can’t trust them to do a calculation in the middle of a task.
You can't trust a person either. Calculating is its own mode of thinking; if you don't pause and context switch, you're going to get it wrong. Same is the case with LLMs.
Tool usage and reasoning and "agentic approach" are all in part ways for allowing LLM to do the context switch required, instead of taking the match challenge as it goes and blowing it.
But my point wasn’t to judge LLMs on their (in)ability to do math - I was only responding to the parent comment’s assertion that they’ve gotten better in this area.
It’s worth noting that all of the major models still randomly decide to ignore schemas and tool calls, so even that is not a guarantee.
If you're going to call all of that not substantial improvement, we'll have to agree to disagree. Certainly it's the most rapid rate of improvement of any tech I've personally seen since I started programming in the early '00s.