DeepSeek V4 went GA on July 20, sticking with the preview's 1.6T MoE architecture, with the core addition being "peak/valley pricing" for industrial-grade inference — Beijing working hours 09:00–12:00 and 14:00–18:00 charge 1x for output, off-peak only $0.87/M. Combined with CSA + HCA pushing KV cache down to 10% of V3.2, V4-Pro finally makes 1M context move from spec sheet to scalable deployment. When DeepSeek V4 open-sourced under MIT in April, the focus was on the 1.6T total / 49B active MoE architecture and 1M context; but the real blocker for production deployment wasn't the parameters — it was the spec-sheet "1M context" that in practice burned out VRAM. V4 GA on July 20 split the architecture story and the business story to answer two different questions: First, why is 1M context cheap? The answer is in the engineering restructuring of the attention layers: CSA (Compressed Sparse Attention, 4x softmax-gated pooling + FP4 lightning indexer top-1024 sparse) and HCA (Heavily Compressed Attention, 128x compression then global dense attention) alternate by transformer layer. At 1M tokens, FLOPs are only 27% of V3.2, and KV cache shrinks to 10% (Flash version 7%). This means V4-Pro's 384K max output + 1M context is the first 1M context that can actually be priced per-token economically. Second, why is the API time-of-use? It's a business-model upgrade: GA introduces peak/valley output pricing — off-peak only $0.87/M, peak doubles to $1.74/M. Even doubled, it's still an order of magnitude cheaper than Claude Opus 4.8 ($15/M) and GPT-5.6 Sol (~$15/M), and by the "Strategy & Operations" benchmark the per-task cost is just $0.03, while Claude Fable 5 is $3.48 — a 116x cost gap. On SWE-bench Verified, V4-Pro scores 80.6%, refreshing the open-weight record and tying Gemini 3.1 Pro; LiveCodeBench 93.5%, Codeforces Elo 3206 also lead. Put those two numbers into the API economics ledger: for the first time, open-source models prove that on engineering cost, closed-source frontier is no longer the default option. Engineering-side "alignment + acceleration" in the same representation space will almost certainly become the standard paradigm for the next generation of open-source LLMs — and DeepSeek has already taken this path all the way to GA.