****DeepSeek V4 Pro/Flash Release & Paper** [developing]
Key Questions
What are DeepSeek V4 Pro and Flash models?
V4 Pro is a 1.6T MoE (49B active) and Flash is 284B (13B active) with 1M context via hybrid attention. They are SOTA open models rivaling GPT-5.4, Opus-4.7, and Gemini 3.1. The HF paper details architecture and costs.
What benchmarks does DeepSeek V4 excel in?
DeepSeek V4 leads open models on MMLU-Pro, agents, coding, and maths. It closes the gap with frontier models at low cost. Pro trained for <$14M, Flash for $4M.
Is DeepSeek V4 optimized for specific hardware?
V4 is adapted for Huawei chips with opto-electronic enhancements. It runs cheaply and fast, supporting agentic AI. This enables broad accessibility.
What are the costs and efficiency of DeepSeek V4?
Pro costs under $14M and Flash $4M to train, drastically reduced vs. rivals. It ships 'good-enough' performance amid rising prices elsewhere. Ideal for agentic pushes without caps.
What applications demonstrate DeepSeek V4?
Uses include Emollick 3D simulations and Al Jazeera integrations. The paper on HF covers million-token intelligence. It supports real-world deployments like Huawei setups.
Open MoE Pro 1.6T (49B active)/Flash 284B (13B active) 1M ctx hybrid attn SOTA open MMLU-Pro/agents/coding/maths rivals GPT-5.4/Opus-4.7/Gemini 3.1; HF paper/costs Pro<$14M Flash$4M/Huawei opto; cheap/fast, Emollick 3D sims, Al Jazeera.