No single open weights model comes close to either Mythos or GPT 5.5.
Nonetheless, running many of the open weights models over a codebase, with an appropriate harness, can provide about the same vulnerability coverage (i.e. each of the open weights models would find a subset of what Mythos or GPT 5.5 could find, but the subsets are not the same).
Despite needing more runs and more time, this may be significantly cheaper, especially if the models are self hosted.
Based on what Anthropic said about Mythos, they also use a quite elaborate harness for finding bugs and vulnerabilities, i.e. not a simple prompt like "find the bugs".
They run repeatedly Mythos on each file of the codebase, many times. They start with more generic prompts, used to determine whether a more thorough analysis of that file is worthwhile. Then they use more specific prompts, to detect various classes of bugs. After it becomes probable that a certain bug exists, they do a final run where the prompt requests a confirmation of the already known bug, perhaps together with a proposed patch or a PoC exploit.
Therefore the efficiency of finding vulnerabilities depends a lot on the harness, not only on the LLM. Also, searching vulnerabilities in a big codebase when paying per token is very expensive, because it requires many runs of the LLM.