Comment by Scene_Cast2
8 days ago
I'm personally heavily testing LLMs on electrical engineering problems. I'm finding that it's not meaningfully better at figuring out what's up than the other models.
To give you an idea - here's a very abridged summary of one sample question (originally a full paragraph): I have a voltage divider with a precision resistor and a thermistor, my voltage reading is off by 17%, where's that coming from. None of the models I tested (including Opus 4.8 and Fable 5) could figure it out.
Did you also test GPT-5.5 Pro web version?
Why is the voltage reading 17% off?
On my (admittedly weird) setup, GPT-5.5 Pro times out.
The reading is off because the thermistor resistance also depends on applied voltage, not just temperature. LLMs couldn't get this even after feeding them multimeter voltage readings, not just ADC readings. They went into guessing much more esoteric things like ADC switched-capacitor input current, burnout-detect current sources or IDACs left enabled, board leakage, leaky cap, etc.
This is the kind of problem I expect Claude to be useless at, and while I could see Gemini Deep Think making a good showing, I'd only bother with ChatGPT Pro. FWIW, I do believe it got the correct answer as one of its first two suggestions (though I am not an electrical engineer, so maybe I am not understanding this given the vague/summarized prompt).
https://chatgpt.com/share/6a2d8c75-56f4-83e8-a61a-301e4c62b1...
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