DeepSeek V4 Network

DeepSeek V4: architecture and rollout signals

DeepSeek V4 is widely discussed as a next-generation Mixture-of-Experts system. Our DeepSeek V4 research brief aggregates public analysis describing a trillion-scale parameter budget with sparse activation per token, a shared expert plus routed experts, and top-k routing to keep inference practical. We treat DeepSeek V4 paper notes and DeepSeek V4 engram references as watch items until an official DeepSeek V4 paper lands.

The DeepSeek V4 brief updates as new signals appear, so teams can calibrate expectations without chasing rumors.

Training discussions emphasize bigger and cleaner corpora, heavier math and code weighting, and improved routing balance. Reported benchmark references include MMLU, HumanEval, GSM8K, and MATH, but we treat these figures as directional until verified by official releases or third-party evaluations. The same notes point to multimodal expansion: strong image understanding today and video generation on the roadmap.

With DeepSeek V4 now in rollout-stage coverage, teams can use the current lineup in production while validating V4 for quality, latency, and cost fit. DeepSeek V3.1 handles general chat and long context, DeepSeek R1 focuses on structured reasoning, Math-7B offers cost-efficient numerical accuracy, Janus-Pro-7B targets multimodal generation, and VL2 excels at OCR and document analysis.

DeepSeek V4 comparisons frequently reference deepseek-v2, deepseek-v2.5, deepseek-coder-v2, and DeepSeek R2, alongside benchmarks versus qwen 3.5, glm 5 (glm5, glm-5), minimax, minimax 2.5, minimax m2.5, seedance 2.0, gemini 3.1 pro, gpt 5.3, grok 4.20, kimi k2.5, and cursor. We keep a concise list of DeepSeek V4 lite discussion points so the DeepSeek V4 brief stays grounded in what teams are actually comparing.