Comparing AI Coding Capabilities via Web Audio Drum Machine Synthesis
Testing Gemini, Fable, and GPT-Astra on their ability to generate functional audio software.
1Beyond Standard Coding Benchmarks
Most flagship AI models are evaluated using rigid coding benchmarks that often fail to reflect practical utility. This experiment shifts focus toward building functional utilities, specifically a drum machine, using the Web Audio API. By testing these models on creative tasks, we can better understand their real-world application in software development.
2Gemini 4 Argon's Aesthetic Approach
Released on September 30, Gemini 4 Argon prioritized visual personality over technical precision. It successfully mimicked classic hardware aesthetics with wood-paneled sides and red LED readouts. However, the model struggled with layout responsiveness during screen resizing and produced thin, fizzy audio output that lacked the depth required for professional-grade musical production.
3Fable 5.1's Methodical Planning
Anthropic's Fable 5.1, part of the Mythos-class tier, demonstrated a highly structured approach by creating a detailed task list before coding. It was the slowest model, taking five minutes to plan, but it added advanced features like track choking. Despite its superior design, the interface relied on difficult-to-use virtual knobs.
4GPT-6 Astra's Audio Superiority
OpenAI's flagship model, Astra, delivered the most impressive audio performance despite a restrained visual design. It avoided the clutter of its competitors and produced a balanced, punchy sound profile. While it lacked the visual color-coding of other models, its synthesis engine provided the most professional-sounding results among the three tested units.
5The Challenge of Virtual Interfaces
Designing interactive elements for browsers remains a significant hurdle for current AI models. While Fable 5.1 opted for authentic-looking knobs, they proved frustrating to manipulate on a screen. Conversely, Gemini 4 Argon utilized sliders, which were more user-friendly but lacked the tactile authenticity expected by users familiar with traditional hardware drum machines.
6Technical Execution and Limitations
None of the models could actually hear the sounds they generated, relying entirely on Web Audio API instructions to synthesize audio locally. Fable 5.1 stood out by implementing a background clock to ensure timing stability, preventing beat drift when the browser tab was inactive. This demonstrated a sophisticated understanding of browser-based audio constraints.
7Synthesizing the Best Results
The experiment reveals a clear trade-off between visual design and audio fidelity. Fable 5.1 created the most functional and aesthetically pleasing interface, while GPT-6 Astra excelled in sound quality and mixing. Combining Astra's robust audio engine with Fable's superior interface design would likely yield the most effective tool for creative users.
8Future of AI-Driven Development
This comparison highlights that benchmark scores do not always correlate with performance in creative coding tasks. While Gemini 4 Argon struggled with technical consistency, the other models showed promise in handling complex requirements. As these models evolve, the ability to bridge the gap between visual design and functional audio will become increasingly critical.