DeepSeek V4 Explained: 1.6 Trillion Parameters & 1 Million Token Context

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DeepSeek V4 just dropped — 1.6 trillion parameters, 1 million token context, fully open source. In this video, I break down exactly what makes V4 different, how the architecture actually works, and whether it can really compete with GPT-5.4, Claude, and Gemini 3.

🔍 WHAT’S COVERED:
00:00 – Intro
01:31 – Who Is DeepSeek and Why Should You Care?
02:59 – The Key Features That Make V4 Different
05:17 – Technical Deep Dive
07:52 – V4 vs. The Frontier: GPT-4o, Llama 3, Claude 3, Gemini 3
10:20 – A Real Demo: What V4 Actually Does
11:48 – Practical Use Cases You Can Actually Run Today
13:26 – The Limitations You Need to Know About

⚡ KEY SPECS:
– V4-Pro: 1.6T total parameters, 49B active per token
– V4-Flash: 284B total, 13B active
– 1,000,000 token context window (default)
– MIT licensed — fully open weights
– V4-Flash: $0.14/M input, $0.28/M output

🧠 ARCHITECTURE HIGHLIGHTS:
✅ Mixture-of-Experts with 384 distributed experts
✅ Compressed Sparse Attention + Heavily Compressed Attention
✅ Manifold-Constrained Hyper-Connections
✅ Trained with the new Muon optimizer
✅ FP4 precision on expert weights

🆚 HOW IT COMPARES:
– Largest open-weight LLM ever released
– Matches GPT-5.4 on coding benchmarks
– ~4x cheaper than Claude 3.5 Haiku on output tokens
– Ties GPT-4o for longest context window
– Text-only (no vision or audio)

💼 BEST USE CASES:
– Long-document Q&A (legal, research, finance)
– Full codebase analysis & agentic coding
– High-volume conversational AI
– Multilingual applications
– Custom fine-tuned deployments

📌 RELATED VIDEOS:
– GPT-5.5 Full Review: https://youtu.be/5iQcpXSpv2c
– Claude vs Chatgpt Comparison: https://youtu.be/Om4bU0iaLAc

💬 Question: Would you actually deploy V4 in production, or is the closed-model ecosystem still worth the premium? Let me know in the comments.

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