- A build-off tested 12 AI models (including GPT-5.6 tiers, Claude, Grok, Muse Spark, and open-weights models) on four coding tasks: a Doom-style raycaster, 3D Rubik's Cube, calculator, and Conway's Game of Life, plus SVG drawing tasks.
- GPT-5.6 Sol excelled at the raycaster with 5/5 playable attempts; Claude Fable 5 led the Rubik's Cube with 5/5 clean solves. GPT-5.6 Luna performed well on latency and cost, answering in ~1 second.
- Open-weights models (e.g., Qwen 3.7 Plus, GLM-5.2) struggled on complex tasks like the raycaster and cube but shone on simple tasks like Game of Life at low cost.
- Grok 4.5 was noted as a cost-effective alternative, performing at 'Opus level' on some tasks, while Muse Spark 1.1 surprised positively but was inconsistent.
- The frontier models (GPT-5.6 Sol, Claude Fable) dominated complex tasks, but results varied by task; open-weights models are viable for well-trodden problems.
- All attempts were made publicly available for user evaluation, emphasizing transparency over objective verdicts.