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DeepSeek-V4
Towards Highly Efficient Million-Token Context Intelligence
DeepSeek-V4 is a preview series of open Mixture-of-Experts LLMs: V4‑Pro (1.6T params, 49B active) and V4‑Flash (284B, 13B active), both with 1M-token context. New hybrid attention (CSA+HCA) cuts long-context compute and KV cache, plus mHC connections and the Muon optimizer for stability. Trained on 32T+ tokens and post-trained with expert specialization + consolidation.
Hybrid attention (CSA + HCA) for long-context efficiency — at 1M tokens, V4‑Pro uses ~27% of single-token inference FLOPs and 10% of KV cache vs DeepSeek‑V3.2
mHC (Manifold-Constrained Hyper-Connections) to improve signal propagation + stability
Muon optimizer for faster convergence and steadier training
Training notes: both models were pre-trained on 32T+ tokens, then post-trained via domain-expert SFT + RL (GRPO), followed by on-policy distillation to consolidate skills.
DeepSeek-V4 was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #143 on the daily leaderboard. DeepSeek-V4 is a preview series of open Mixture-of-Experts LLMs: V4‑Pro (1.6T params, 49B active) and V4‑Flash (284B, 13B active), both with 1M-token context. New hybrid attention (CSA+HCA) cuts long-context compute and KV cache, plus mHC connections and the Muon optimizer for stability. Trained on 32T+ tokens and post-trained with expert specialization + consolidation.
On the analytics side, DeepSeek-V4 competes within Artificial Intelligence — topics that collectively have 467.2k followers on Product Hunt. The dashboard above tracks how DeepSeek-V4 performed against the three products that launched closest to it on the same day.
Who hunted DeepSeek-V4?
DeepSeek-V4 was hunted by Luo. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of DeepSeek-V4 including community comment highlights and product details, visit the product overview.
Luo
DeepSeek‑V4 includes two open MoE models built for extreme long-context work:
DeepSeek‑V4‑Pro: 1.6T params (49B activated), 1M tokens
DeepSeek‑V4‑Flash: 284B params (13B activated), 1M tokens
What’s new under the hood:
Hybrid attention (CSA + HCA) for long-context efficiency — at 1M tokens, V4‑Pro uses ~27% of single-token inference FLOPs and 10% of KV cache vs DeepSeek‑V3.2
mHC (Manifold-Constrained Hyper-Connections) to improve signal propagation + stability
Muon optimizer for faster convergence and steadier training
Training notes: both models were pre-trained on 32T+ tokens, then post-trained via domain-expert SFT + RL (GRPO), followed by on-policy distillation to consolidate skills.