This is again will be very funny race to watch. Right now you can fairly do lots of reverse enginnering or "hacking" with off the shelf Chinese models, and they are very capable (GLM3 for example) which can be run locally if you stomach the hardware. Tools like rea are also becoming more popular. If openai continues to be pedantic, they will lose the technical base given how close is the gap getting.
The company I work for has a plan B to switch to alternative, self-hosted models if providers pull off strange moves, such as drastic pricing changes. We have already switched from one provider to another once, and we do not want to get burned again. We do not reverse engineer, just regular development.
Until Kevin quits and you need to modify his work
Right, because admin is some obscure work in 2026 and no one knows how to do it.
Have you tried Qwen3.8-Flash-Next? I found it significantly better in anything I tested than GLM5.3-Flash, but the thing is you either use Alibaba cloud or you host it yourself (no lesser quant than unsloth 4.5 or mx4/nvfp4 and kv no more quantized than int8).
It is great if you have enough RAM. I have enough to run it, but not enough for prefix cache. 27b is so much faster with long term tasks.
The Qwen3.8-Flash-Next model is ok for reverse engineering. It can definitely solve simple tasks, but it gets easily confused, and burns tokens like crazy.
I've been sick with stomach flu the past few days, so I put together a small benchmark for all the top AI models where I ask them to reverse engineer a very simple small crackme (https://www.crackmes.one/crackme/6ab4ce5195b976f8f1300b54) using Ghidrasql (https://github.com/0xeb/ghidrasql)
(As an aside: I think representing tools for AI as virtual SQL tables is much better than traditional MCP servers, and that this approach deserves a lot more attention. Props to Elias Bachaalany for thinking out of the box and putting it together).
The results of the benchmark are below. It's sorted by cost, but I added notes about each AI model for myself. The notes try to talk about intelligence, speed, and cost. Intelligence is a subjective measure which I assign to each model by reading its thinking tokens and watching its tool calls. You can definitely tell which models are more intelligent, even on this small benchmarks, by their tool use, false starts, and how easily they fall for the simple misdirection in the crackme.
(gist is more readable on mobile) https://gist.github.com/markoglasgow/8812c19b6fbffed30b2661b...
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```The latest Deepseek-Flash model was the most impressive of the open models. Intelligent, fast, cheap. What more do you want? I think the Xiaomi MiMo - Flash model did well, but it might be a fluke, given how poorly the Xiaomi MiMo Pro model performed (it wandered off on a million side quests).
What's also interesting is how competitive Luna is to the lower end Chinese models. It's quite a bit more intelligent, burns a lot less tokens, is plenty fast and very cheap. After doing this experiment, I don't think it's a foregone conclusion that the American AI labs will be having the bottom of the market eaten away by the Chinese models. It seems they (at least OpenAI) can compete on the bottom end as well.
Finally, the benchmark's results reinforce a few things people have been saying about Sonnet and Terra, namely, they're not competitive for a range of tasks. For easier tasks, Haiku/Luna are plenty capable. For harder tasks, Opus and Sol are so much more intelligent, that they can get them done quicker and cheaper. Maybe there is a place for them in the "Opus designs and Sonnet implements" workflows, but in that case, why not "Opus designs and Deepseek implements?"
PS Code harness used was Claude Code. $20 in openrouter credits was used to access the models. Kimi K3 and Deepseek were also accessed directly via the API and then compared to Openrouter. Kimi K3 specifically was much faster on Openrouter, and the difference in output tokens to get the same results for Deepseek/Kimi makes me think the Openrouter providers are hosting significantly quantized versions of the model. Goes to show that Moonshot.ai's acceptance testing of providers is a useful thing, otherwise your model might look artificially bad when it's hosted as heavily quantized by providers who dont care about quality, just top-line cost and speed.
For the past few days I've been trying to use rea to reverse engineer my Samsung Galaxy Buds 4 Pro to get seamless functionality working outside of the Samsung ecosystem with no luck (the buds 4 pro require Samsung devices + a Samsung account for seamless to work, I only have a Galaxy phone + my macbook). I think we're still far away from having something that works that well.