Focusing on long running agentic workloads. Running a frontier open model as an agent today breaks in predictable ways. The model stops. You reprompt. The trace blows past the context budget, or the KV cache fills the GPU, or tool-call round trips degrade halfway through a long task. V4 is built to fix these known failures, and point the way for the community to follow.

This post covers three things: what the architecture does differently to make long-context inference cheap, the agent-specific post-training decisions that compound on top of it, and some takeaways from the paper that help reason about these changes.

A 1M context window is just capacity, not performance. Whether you can use it depends on the cost of every forward pass at that depth. For an agent running a long tool-use trajectory (a SWE-bench task, a multi-step browse session, a terminal session with hundreds of commands), every tool result is appended to the context, and every subsequent token pays the full attention cost against everything that came before.

Two numbers matter: single-token inference FLOPs and KV cache size. Both grow with sequence length. At 1M tokens, DeepSeek-V4-Pro requires 27% of single-token inference FLOPs compared with DeepSeek-V3.2, so it runs faster on the same hardware. It also uses 10% of the KV cache memory. V4-Flash drops these numbers even further: 10% of the FLOPs and 7% of the KV cache.

If we compare the KV cache memory against a established architecture like grouped query attention with 8 heads, stored in the usual bfloat16 format, DeepSeek v4 requires roughly 2% the cache size.

Source link