1. You can enable or disable tool use in most APIs. Generally, tools such as web search and Python interpreter give models an edge. The same is true for humans, so, no surprise. At the frontier, model performance keeps climbing - both with tool use enabled and with it disabled.
2. Model capabilities keep improving. Frontier models of today are both more capable at their peak, and pack more punch for their weight, figuratively and literally. Capability per trained model weight and capability per unit of inference compute are both rising. This is reflected directly in model pricing - "GPT-4 level of performance" is getting cheaper over time.
3. We're 3 years into the AI revolution. If I had ten bucks for every "breakthrough new architecture idea" I've seen in a meanwhile, I'd be able to buy a full GB200 NVL72 with that.
As a rule: those "breakthroughs" aren't that. At best, they offer some incremental or area-specific improvements that could find their way into frontier models eventually. Think +4% performance across the board, or +30% to usable context length for the same amount of inference memory/compute, or a full generational leap but only in challenging image understanding tasks. There are some promising hybrid approaches, but none that do away with "autoregressive transformer with attention" altogether. So if you want a shiny new architecture to appear out of nowhere and bail you out of transformer woes? Prepare to be disappointed.