C33D
89K posts
C33D
@c33dbench
Building the future of 3D generation. C33D is an experimental engine unifying natural logic, seamless modeling, and precise spatial visualization.
When asked to make a best possible version of a cat without making mistakes GPT 5.6 Sol and Fable 5 did output the best result, Gemini 3.5 Flash and GLM 5.2 did manage to output working graphs whilst Grok 4.5 did not manage to output graph that has working 3d preview. More details bellow.
Parametric cat by @Kimi_Moonshot 2.7
When asked to make a best possible version of a cat without making mistakes GPT 5.6 Sol and Fable 5 did output the best result, Gemini 3.5 Flash and GLM 5.2 did manage to output working graphs whilst Grok 4.5 did not manage to output graph that has working 3d preview. More details bellow.
🧵 C33D — Evaluating Parametric Design Intelligence in AI Evaluating AI spatial reasoning has a major blind spot: existing benchmarks focus entirely on static geometry or code compilation. They do not evaluate if the model's design survives changes. We are launching C33D, an open-science platform and benchmark for AI-native parametric CAD. 🔗 github.com/3esign/c33d
🧵 C33D — Evaluating Parametric Design Intelligence in AI Evaluating AI spatial reasoning has a major blind spot: existing benchmarks focus entirely on static geometry or code compilation. They do not evaluate if the model's design survives changes. We are launching C33D, an open-science platform and benchmark for AI-native parametric CAD. 🔗 github.com/3esign/c33d
🧵 C33D — Evaluating Parametric Design Intelligence in AI Evaluating AI spatial reasoning has a major blind spot: existing benchmarks focus entirely on static geometry or code compilation. They do not evaluate if the model's design survives changes. We are launching C33D, an open-science platform and benchmark for AI-native parametric CAD. 🔗 github.com/3esign/c33d
🧵 C33D — Evaluating Parametric Design Intelligence in AI Evaluating AI spatial reasoning has a major blind spot: existing benchmarks focus entirely on static geometry or code compilation. They do not evaluate if the model's design survives changes. We are launching C33D, an open-science platform and benchmark for AI-native parametric CAD. 🔗 github.com/3esign/c33d
🧵 C33D — Evaluating Parametric Design Intelligence in AI Evaluating AI spatial reasoning has a major blind spot: existing benchmarks focus entirely on static geometry or code compilation. They do not evaluate if the model's design survives changes. We are launching C33D, an open-science platform and benchmark for AI-native parametric CAD. 🔗 github.com/3esign/c33d
🧵 C33D — Evaluating Parametric Design Intelligence in AI Evaluating AI spatial reasoning has a major blind spot: existing benchmarks focus entirely on static geometry or code compilation. They do not evaluate if the model's design survives changes. We are launching C33D, an open-science platform and benchmark for AI-native parametric CAD. 🔗 github.com/3esign/c33d
🧵 C33D — Evaluating Parametric Design Intelligence in AI Evaluating AI spatial reasoning has a major blind spot: existing benchmarks focus entirely on static geometry or code compilation. They do not evaluate if the model's design survives changes. We are launching C33D, an open-science platform and benchmark for AI-native parametric CAD. 🔗 github.com/3esign/c33d
🧵 C33D — Evaluating Parametric Design Intelligence in AI Evaluating AI spatial reasoning has a major blind spot: existing benchmarks focus entirely on static geometry or code compilation. They do not evaluate if the model's design survives changes. We are launching C33D, an open-science platform and benchmark for AI-native parametric CAD. 🔗 github.com/3esign/c33d
🧵 C33D — Evaluating Parametric Design Intelligence in AI Evaluating AI spatial reasoning has a major blind spot: existing benchmarks focus entirely on static geometry or code compilation. They do not evaluate if the model's design survives changes. We are launching C33D, an open-science platform and benchmark for AI-native parametric CAD. 🔗 github.com/3esign/c33d