One million tokens in context used to demand 4 GB of VRAM per stream in KV cache alone.
DeepSeek-V4 reduces it to 82 MB: a 98 percent collapse in memory footprint. Here is the three-part architectural co-design that made it possible: 1. Compressed Sparse Attention + FP4
DeepSeek V4 Pro just scored 87.9 on Terminal Bench 2.1.
That matters because this isn’t another “AI answers questions better” benchmark. It tests whether an AI can actually finish tasks inside a computer terminal. And DeepSeek built V4 Pro around exactly that.
DeepSeek-V4 Pro Max vs Claude Opus 5 vs Grok 4.5 Max
Testing 2M+ Token Context Retrieval & Complex Code Synthesis: 🔹 DeepSeek-V4 Pro Max: Fastest inference & cost-efficiency for massive codebases (98.4% Needle-In-A-Haystack accuracy). 🔹 Claude 5 Opus: Unrivaled structural
DeepSeek Harness is an agent harness where every component is a plugin, built on Cordis for spatiotemporal composability.
Run it via `npx deepseek-ai/dsh web` to get a local Web UI at port 3080. Explore it here:
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