Bugs the other models were benchmarked on are from the corpus that Mythos found. So Mythos might have 100% in this benchmark.

Although the benchmark had 100$ budget cap and rudimentary tooling so probably a bit less than 100%.

GPT-5.5-pro attemted only 4 problems out of 9 before the budget ran out and got 2 of them right.

It's a shame that the author didn't try GPT-5.5-pro on all 9 just for completeness, pehaps on subscription to save money.

Also, with regard to tools, I originally ran a batch of several models in a full-featured agent (and whatever tools the agent provides), and they didn't perform better than the basic minimal harness with just read and grep. They chewed more tokens but didn't find more bugs. I'm currently doing tests with more advanced tools, like tree-sitter so the model can better understand execution and data flow and semgrep (which is almost cheating, since it finds bugs on its own, but worth a try since models can still be useful in helping rule out false positives and suggest mitigations). When I've got time for it, I'll also give them a full dev environment with compiler, debugger, and maybe fuzzer, and a loop that iterates through a security bug hunting checklist (since a single prompt and context window can't handle that much complexity at once).

At the time a GPT subscription didn't include Pro usage in the rolling limits. It was billed at API rates. Does it now?

If anyone wants to fund the other five cases (~$125), I'll run them. I find that an unrealistic cost, though...simply not useful data. I'm certainly not going to spend $23 per file to audit a project with hundreds or thousands of files. I don't know anyone who would.

Also note that it was $100 cap per model, and the next most expensive model was GPT 5.5 at a 20th the price per case, about ten bucks for the whole batch.