DeepSeek V4.1 Flash-1017 vs GPT-6.1 Sol: Hands-On Code Generation Benchmark

The article compares DeepSeek V4.1 Flash-1017 and GPT-6.1 Sol using identical prompts on three classic frontend coding challenges, running both models at high reasoning mode. The author records execution times and evaluates output quality, focusing on practical differences for real-world development.

Case 1: Rainy Night Convenience Store (Three.js)

The task: build a Three.js scene of a Japanese convenience store on a rainy night with warm interior light, cold exterior rain, wet ground reflections, automatic doors, and mouse rotation/zoom — no external assets allowed.

DeepSeek produced "月見マート" (Tsukimi Mart). The atmosphere was striking: a wide street corner, warm yellow interior glow against cold blue streets, wet pavement reflecting the doorway lights. However, the task took 35 minutes 5 seconds .

GPT-6.1 Sol delivered "小春便利店" (Xiaochun Convenience Store) in 6 minutes 28 seconds . It took a diorama approach — a tabletop miniature — and added extra controls: rain toggle, rain sound, and an indoor-view button that hides the roof and dollies the camera inside. The author notes GPT's speed has improved noticeably since DeepSeek's release, speculating that competitive pressure may have freed up compute.

Verdict: For pure rainy-night atmosphere the author prefers DeepSeek, but the 35-minute vs 6-minute gap makes GPT far more practical for daily work.

Case 2: Dynamic Weather Cards (HTML/CSS/JS + SVG)

Task: create four animated weather cards (wind, rain, sun, snow) using only native HTML, CSS, JavaScript, and SVG, with pause/resume buttons and an animation-intensity slider.

DeepSeek: 6 minutes 40 seconds . It devoted most of each card to scenic illustration — especially the rainy-night card with a warm window and puddle ripples — which looks impressive as a standalone screenshot.

GPT-6.1 Sol: 4 minutes 27 seconds . It enforced a consistent layout across all cards: temperature and icon fixed at top, animated scene in middle, supplementary info at bottom. This uniformity means a developer can drop the component into a project without hunting for where the temperature lives on each card.

Verdict: GPT's output is more production-ready; DeepSeek's is more artistic but less consistent.

Case 3: Pelican Riding a Bicycle (SVG Animation)

Prompt: "Create an HTML file with an SVG 2D animation of a pelican riding a bicycle; no testing needed." The key challenge is leg pedaling — feet must stay on pedals, knees must articulate naturally.

DeepSeek: 2 minutes 40 seconds . Bold, expressive style — large orange beak, long legs, bird and bike centered. Recognizable at a glance.

GPT-6.1 Sol: 2 minutes 28 seconds . Added background context — lighthouse, sailboat, palm trees — gave the pelican a scarf, and included pause and playback-speed controls at the top.

Source inspection shows both models correctly calculated knee positions from hip and pedal coordinates. GPT additionally bound wheel rotation and ground translation to a single distance variable. The author suggests watching the pedals in the side-by-side videos to judge smoothness.

Summary

Across all three tasks, GPT-6.1 Sol proves more stable: better layout details, thoughtful extra interactions, and significantly faster generation on complex 3D scenes. Its output is easier to pick up and iterate on.

However, DeepSeek V4.1 Flash-1017's pricing is extremely attractive — no account/subscription hassles, pure pay-as-you-go, no wasted monthly quotas. For quick idea validation or routine tasks, it imposes almost zero psychological or financial barrier.