DeepSWE is slightly flawed in the sense that is uses only its own harness and that causes issues on models that are not correctly supported by it. There's huge amount of evidence that the harness plays a big role in how these models work and yet DeepSWE entirely removes that (and has probably only tested that it works fine with some favourite model of them).
There's also issues with cost calculation (as that harness doesn't use caches) and so on as reported on their github issues.
None of the benchmarks are perfect, but that does explain a lot of the variations between benchmarks.
It can run and the token generation is fast enough, but the prompt processing is so slow that it makes them next to useless. That is the case with my M3 Pro at least, compared to the RTX I have on my Windows machine.
This is why I'm personally waiting for M5/M6 to finally have some decent prompt processing performance, it makes a huge difference in all the agentic tools.
There's also issues with cost calculation (as that harness doesn't use caches) and so on as reported on their github issues.
None of the benchmarks are perfect, but that does explain a lot of the variations between benchmarks.