New DeepSeek V4 Update is INSANE! 🤯
DeepSeek V4 has been released in two variants (Pro and Flash), featuring 1.6 trillion parameters, a 1 million token context window, and extremely low pricing. The video argues this represents a major market shift, making enterprise-grade AI accessible to developers and businesses at a fraction of competitor costs. Three practical use cases are demonstrated: content strategy, member onboarding personalization, and business intelligence extraction.
Summary
The video opens with a bold claim that DeepSeek V4 fundamentally changes the AI landscape, highlighting its 1.6 trillion parameters, 1 million token context window, open-source availability, and disruptively low pricing — achievements the presenter notes came from a Chinese AI lab rather than OpenAI or Anthropic.
Two models were released simultaneously: V4 Pro and V4 Flash. V4 Pro has 1.6 trillion total parameters with approximately 49 billion active at any time via a Mixture of Experts (MoE) architecture, designed for deep reasoning and complex multi-step tasks. V4 Flash is the lightweight counterpart with 44 billion total parameters and around 13 billion active, optimized for fast, high-volume, affordable automation tasks. Both are accessible via chat.deepseek.com, the API, or open weights on Hugging Face.
A central focus of the video is the 1 million token context window, which the presenter describes as a genuine capability shift rather than a marketing figure. This allows users to feed entire codebases, full books, or months of business data — support tickets, CRM records, email threads — into a single prompt without needing to chunk, summarize, or stitch data together. The presenter argues that eliminating the chunking problem removes one of the biggest friction points in AI automation workflows.
On pricing, V4 Flash costs approximately $0.028 per million tokens on cache hits, described as cheaper than nearly anything else on the market. V4 Pro is also significantly cheaper than comparable closed models. The presenter argues this doesn't just make DeepSeek a better option — it changes the economic calculus of what's worth building with AI.
Three detailed use cases are walked through with example prompts. First, content strategy for creators: loading all YouTube transcripts and prompting the model to identify high-engagement topics, content gaps, and generate a 12-video quarterly content plan. Second, personalized member onboarding: feeding a full training library and a new member's intake form to generate a custom 30-day learning path tailored to that individual's goals and experience. Third, business intelligence from raw data: dropping in three months of support tickets, sales call transcripts, and lost deal notes to extract top objections, friction points, and feature recommendations.
The video briefly explains the MoE architecture using an analogy: a team of 100 specialists where only 3-4 relevant experts activate per task, enabling top-tier output at reduced compute cost. Benchmark scores cited include 87.5% on MMLU Pro and 93.5% on CodeBench. The presenter concludes that for practical business tasks — writing, coding, data analysis, automation — DeepSeek V4 competes with best-in-class models at a fraction of the price.
Key Insights
- The presenter argues that DeepSeek V4's 1 million token context window effectively eliminates chunking — one of the biggest friction points in AI automation — allowing entire codebases, books, or months of business data to be analyzed in a single prompt without preprocessing.
- V4 Flash is priced at approximately $0.028 per million tokens on cache hits, which the presenter claims is cheaper than almost anything else currently on the market, fundamentally changing the economics of what AI-powered systems are worth building.
- DeepSeek V4 uses a Mixture of Experts architecture with 1.6 trillion total parameters but only activates a small fraction — around 49 billion for Pro — on any given query, which the presenter explains is how the model achieves top-tier output at a fraction of normal compute cost.
- The presenter demonstrates a business intelligence use case where three months of support tickets, sales transcripts, and lost deal notes are fed into a single prompt to extract top objections, friction points, and feature recommendations — work the presenter says previously took weeks of manual review.
- DeepSeek V4 scores 87.5% on MMLU Pro and 93.5% on CodeBench, placing it in the top tier of available models, and the presenter argues it competes directly with best-in-class closed models for practical business tasks at a dramatically lower price.
Topics
Full transcript available for MurmurCast members
Sign Up to Access