Yes, the models have gotten better at using tools because tech companies have poured an insane amount of money into improving tools and integrating them with LLMs. Is this because the models have actually improved, or because the tools and integration methods have improved? I don’t think anyone actually knows.
But, isn't improving tools and the LLM's integration with them improving the model?
Caveat that we don't fully understand how human intelligence works, but with humans it's generally true that skills are not static or siloed. Improving in one area can generate dividends in others. It's like how some professional football players improve their games by taking ballet lessons. Two very different skills, but the incorporation of one improves the other as well as the whole.
I would argue that narrowly focusing on LLM performance via benchmarks before tool use is incorporated is interesting, but not particularly relevant to whether they are transformative, or even useful, as products.
Re:Reasoning Is Not Model Improvement
by XenophileJKO on Thursday October 23, 2025 @05:04PM
(#45687139)
The models have improved. They are using "arbitrary tools" better.
This improved think->act->sense loop that they now form, exponentially increases the possible utility of the models. We are just starting to see this with gpt-5 and the 4+ series of Claude models.