DeepSeek-V4-Pro Nails an Apple-Style Page Clone in One Shot but Fumbles a Pelican on a Bike
Agentic coding tools are being sold as general-purpose, but this test shows a sharp capability cliff: the same model that autonomously builds a production-grade marketing page can't draw a cartoon bird on a bike without human correction. Teams betting on AI-driven frontend work need to map tasks to model strengths, not assume uniform competence.
A hands-on test of DeepSeek-V4-Pro with the DeepSeek Harness agent reveals a model that is strong in uneven ways. Given a detailed, multi-thousand-word prompt to replicate Apple's AirPods Pro page — including browser research, responsive breakpoints, scroll-driven animations, and Playwright self-verification — the system produced a polished Next.js page in one shot with no manual fixes. The result was good enough to pass visual comparison against the real site.
That same model, asked to create a simple SVG animation of a pelican on a bicycle, generated a figure with missing handlebar grip and the wrong orientation. Even after self-correction via an image-recognition plugin, it missed obvious spatial errors until a human pointed them out. A side-by-side test with ByteDance's doubao-seed-evolving model showed the latter handled motion coordination and spatial relationships far more naturally on the same task.
The contrast points to a pattern: DeepSeek-V4-Pro excels at long-chain engineering tasks with clear constraints and repeated verification loops, but stumbles on open-ended visual problems that demand spatial common sense. The test also notes a steep price increase — from 4 yuan to 43 yuan per 100 million tokens — raising questions about cost-effectiveness for heavy usage.
The 100% cache hit on the Apple clone raises a question about how much of the output was reconstructed from training data versus reasoned from the reference page — a distinction that matters for copyright and originality in commercial work.
DeepSeek Harness's self-verification loop caught the missing handlebar grip but missed the orientation error, showing that vision-based self-critique still has blind spots a human spots instantly.
The price increase from 4 to 43 yuan per 100M tokens is steep enough to change the economics of agentic workflows that burn through tokens in long verification chains.
Doubao's stronger performance on the pelican task — a model the tester hadn't used in a long time — suggests the Chinese model landscape is shifting faster than casual observers track, with under-the-radar models catching up on specific capabilities.