<0.2 → Poor – Misses core intent; largely irrelevant or incorrect.
<0.4 → Weak – Partially relevant; significant omissions or errors.
<0.6 → Fair – Covers main points but lacks completeness or precision.
<0.8 → Good – Mostly accurate; minor gaps or deviations.
<=1.0 → Excellent – Fully aligned; precise, comprehensive, and faithful to intent.
Here is the scenario list (prompts are much more detailed): dragon-bottle-stopper
editing-param-mid-conv
editing-parametric-enclosure
editing-swap-material-param
editing-text-edit-cube
multi-turn-bird-house
multi-turn-dice-tower
multi-turn-modular-planter
multi-turn-phone-stand
multi-turn-shelf
one-shot-bookend
one-shot-cable-clip
one-shot-chess-queen
one-shot-coaster
one-shot-coffee-cup
one-shot-dog-tag
one-shot-dragon-figurine
one-shot-hex-bracket
one-shot-keychain-fob
one-shot-low-poly-tree
one-shot-pegboard-hook
one-shot-pi4-case
one-shot-threaded-jar
[0]: https://grandpacad.com Model + config CodeErr/gen Cost/gen Median time Quality
gemini-3.5-flash, low 0.71 $0.18 68s baseline
GLM 5.2, reasoning high 0.61 $0.18 289s -6.0%
GLM 5.2, reasoning off 1.52 $0.10 126s -13.6%
Although it is cheaper, it is significantly slower, and results are worse overall. Surprisingly - high reasoning produces less code errors than gemini 3.5 flash, but when I actually look at the models they are worse.
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Working on AI 3D modeling software - https://grandpacad.com
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