DeepSeek V4 and Hunyuan Hy3 handle 90% of coding refactors at 80x lower cost than Opus

A developer recently shared a real-world experiment proving that cheap open-weight AI models can handle the vast majority of routine coding work. Using DeepSeek V4 and Hunyuan Hy3 preview — both with 21B active parameters and costing roughly $0.18 per million input tokens (about 80x cheaper than OpenAI's Opus) — they mass refactored a 120-file FastAPI service. The process involved 400 steps and consumed 2 million tokens, costing a total of just $3. The AI completed 360 of those steps without any human input in under an hour, with Tencent reporting a 99.99% step success rate across 495 production runs — a number that matched the developer's own experience for repetitive refactors.

However, the remaining 40 steps — the 'hard 10%' — required escalation to Opus. One failure was particularly telling: the cheap model confidently introduced a deadlock into an async event handler. While the developer found it amusing, it underscores that complex logic, concurrency issues, and nuanced architecture still demand human oversight or more advanced models. The experiment highlights a clear division of labor: open-weight models like DeepSeek V4 and Hunyuan Hy3 can automate the boring 90% of coding at dramatically lower cost and faster latency, but the hard 10% — debugging, novel design, safety-critical changes — remains the domain of top-tier models or human engineers.