DeepSeek V4 Drops — 1T Params, Open-Weight, Natively Multimodal

The Headline Number Is Misleading — In a Good Way

1 trillion parameters. Before you panic about hardware requirements, here's the thing: you don't need to run all of them.

What Happened

DeepSeek V4 launched in early March as the most ambitious open-weight model ever released. The 1T parameter count is the total model size, but thanks to its MoE (Mixture-of-Experts) architecture, only about 32 billion parameters activate per token. The rest are specialists waiting for their turn.

This is DeepSeek's first natively multimodal model. Unlike earlier approaches that bolted vision capabilities onto a text model, V4's multimodal architecture was built into pre-training — processing text, images, and video without adapter layers or quality degradation. Context window exceeds 1 million tokens.

Why It Matters

V4 is released under a permissive commercial license. You can download it, fine-tune it, and deploy it without licensing fees. DeepSeek V3 proved open-weight models can compete with the best proprietary offerings. V4 extends that proof to multimodal and trillion-scale.

Combined with efficient local runtimes like Ollama, developers can now build high-capability applications without any API dependency. This fundamentally changes the calculus for startups deciding between building on proprietary APIs vs. self-hosting.

Going Deeper

V4 introduces Engram conditional memory — a mechanism that helps agents maintain more intelligent context across long conversations. The model also features a multi-modal input window designed specifically for deep reasoning and coding tasks, making it a direct competitor to GPT-5 class models.

Bottom Line

The biggest open-weight firework yet. The closed vs. open debate just got a lot more interesting.