DeepSeek-Led Qwen Team Matches Solo Results at Higher Cost
In an experiment reported on Reddit, DeepSeek-V4.1-Flash coordinated four Qwen3.5-9B workers to reverse engineer two small C binaries and rewrite their functions in Python. The team completed all 38 and 56 tested functions, but required 86 and 100 minutes and cost $2.97 and $3.60, versus 14 and 54 minutes and $0.87 and $3.28 for DeepSeek working alone. The lead revised worker instructions and added a signature-and-offset lookup tool, while about 40% of functions still went back to it.
The reported results show that a lead model can organize smaller local models into a working multi-agent pipeline, but also show substantial coordination overhead on these two small, unstripped binaries.
| Reverse Engineer Anything blew up recently but, every impressive reverse engineering result we’ve seen uses (closed) frontier models. We wanted to know whether small local models can do useful work here if a bigger model organizes them, hopefully cutting cost and allowing a broder range of people to Reverse Engineer Anything :) Setup To begin with a proof of concept, the targets were two small C programs we generated with Claude Code and compiled. The team only ever saw the binaries. As a baseline, we also ran DeepSeek (the lead) alone on each tasks. The team work is pure overhead on smaller jobs, the lead has to organize and fix the smaller models, taking more time than if it had just worked alone. The most interesting part For the shooter game, the lead ended up changing the instructions of the smaller models 3 times. Caveats You can find the full write-up here: https://huggingface.co/blog/ybelkada/reverse-engineering-anything-with-small-local-mode submitted by /u/HauntingMoment [link] [comments] |
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