I Asked Claude, ChatGPT, and Gemini to Build a Simulation, and One Winner Was Obvious

Why It Matters

The test highlights stark differences in LLM code‑generation quality, influencing developer productivity and subscription choices in the competitive AI market.

Key Takeaways

• Claude generated fully functional, textured solar system in one file
• Gemini delivered fast code but had interaction bugs, low detail
• ChatGPT output placed all planets at origin, unusable
• Claude used procedural JavaScript textures, achieving realistic planet looks
• Free Claude outperformed paid Gemini and ChatGPT for developer tasks

Pulse Analysis

Large language models are increasingly positioned as on‑demand coding assistants, yet reliable benchmarks remain scarce. By asking three leading LLMs—Claude, Gemini, and ChatGPT—to create a browser‑based solar‑system explorer, the author crafted a practical test that stresses physics, graphics, and user interaction. The prompt emphasized realistic orbital mechanics, polished visuals, and a self‑contained HTML file, forcing each model to translate abstract scientific concepts into concrete Three.js code without external assets.

Claude Sonnet 4.6 emerged as the clear winner. It not only generated accurate orbital distances and rotations but also procedurally crafted high‑fidelity planet textures within a single script, even adding an asteroid belt for completeness. Gemini’s output, while faster, suffered from broken click‑to‑focus behavior, simplistic gradients, and a zoom lock that required a page refresh. ChatGPT’s attempt collapsed at the fundamentals, placing every celestial body at the origin due to a unit‑conversion oversight, and it failed to self‑diagnose the error.

The implications extend beyond a novelty demo. Developers evaluating AI‑assisted coding tools now have concrete evidence that free access to Claude can outperform paid tiers of competing services, potentially reshaping subscription strategies. As enterprises seek to embed LLMs into product pipelines, the ability to deliver production‑ready code on the first try becomes a decisive factor, reinforcing Claude’s positioning as the go‑to model for task‑oriented developers.