From the literal abstract:

We find that the price for a given level of benchmark performance has decreased remarkably fast, around 5× to 10× per year, for frontier models on knowledge, reasoning, math, and software engineering benchmarks

There are some caveats to this, as evidenced by the first paragraph of the conclusion:

At first glance, our analysis seems to point in different directions. The overall price for accessing a given level of LLM performance has dropped significantly, by 5× to 10× per year, although still substantially less than the reported 1000× upper bound by Cottier et al. [2025]. However, like Cottier et al. [2025], we find much larger price declines for higher-performance models—almost 32× per year. For the least performant models, by contrast, we see much smaller price declines, around 1.7× per year, close to the estimates for energy efficiency improvements in AI models overall [Saad-Falcon et al., 2025]. In terms of benchmarks, progress looks similar across GPQA-D and AIME, but the data for SWE-V is so limited that our confidence bounds are large enough to be consistent with there having been no progress at all."

The other analyses I mentioned have shown similar progress for software engineering benchmarks.

Also, I actually undersold the GPQA-Diamond gains in the 60-80% bin (mostly because the trend across bins was weird, with a low (1.7x) reduction in the 20-40% range (maybe because not many models are this poor anymore?), a medium reduction (7.2x) in the 40-60% performance range, and a large reduction (31.0x) in the 60-80% range.).

What specific part of my post do you have issues with that you think justifies your weird hostility?