The article compares OpenAI's GPT‑5.5 and DeepSeek V4 on architecture, inference efficiency, benchmark performance, pricing, and ecosystem openness, offering scenario‑based recommendations to help developers choose the model that best fits their cost, performance, and deployment needs.
A Chinese research team’s 2024 "density law"—which predicts that the parameters needed for a given LLM performance halve every 3.5 months—has been independently validated by Meta’s scaling ladder, METR’s time‑horizon report, and subsequent analyses, revealing a unified exponential growth curve that reshapes expectations for inference cost, edge AI feasibility, and optimal model‑development strategies.
The article analyses DeepSeek’s unconventional low‑price, high‑latency strategy, its token‑pricing and KPI trade‑offs, and compares its performance, hardware choices, and market share with Anthropic, OpenAI, Google and other AI providers, while also discussing the rise of inference‑as‑a‑service and rumors about DeepSeek R2.
DeepSeek V3 cuts training to 2.788 M H800 GPU‑hours with FP8 mixed‑precision and a fully optimized framework, slashes token costs by 96% versus ChatGPT O1, and its efficient inference and model‑compression techniques are reshaping AI‑agent development, spurring demand for low‑latency, high‑bandwidth optical modules and edge‑computing infrastructure.
