On April 24, DeepSeek released V4-Pro (1.6T/49B activated) and V4-Flash (284B/13B activated), with the core change being the introduction of Compressed Sparse Attention (CSA) + Heavy Compressed Attention (HCA), combined with mHC hyper-connections, reducing per-token inference FLOPs to 27% of V3.2 and KV Cache to 10% under 1 million token context.
Long-context inference used to be an open-source model禁区 — KV storage grows linearly with context, 128K is most models' limit. V4 has broken through this wall. Both support Thinking/Non-Thinking dual modes, with max output of 384K, base context window unified at 1 million tokens.
On the Arena AI coding leaderboard, V4-Pro Thinking ranks third with 1456 Elo (behind GLM-5.1 at 1534 and Kimi K2.6 at 1529), Codeforces score 3206, surpassing GPT-5.4 xHigh's 3168 — the first time an open-source model has substantively led closed-source frontier models on competitive programming leaderboards. But MRCR 1M retrieval (83.5 vs Opus 4.6's 92.9) shows long-context precise retrieval is still Opus's home turf; V4's advantage is efficiency, not comprehensive surpassing.
Flash-Max's cost-performance is particularly outstanding: output price only $0.28/M (Pro is $3.48/M), LiveCodeBench 91.6 with minimal gap from the Pro version. The upgrade from MIT to Apache 2.0 also provides clearer patent protection for enterprise commercial deployment.
What's most noteworthy about DeepSeek V4 isn't just the benchmarks, but that the efficiency curve has been completely changed. 27% FLOPs and 10% KV Cache mean ultra-long-context inference cost can for the first time be on par with short-context models — a substantive positive for applications needing to handle long codebases, long documents. The "million tokens, use at will" era the open-source community has long awaited may begin here.