# DeepSeek-V4: a million-token context that agents can actually use

DevFeed: [DeepSeek-V4: a million-token context that agents can actually use](<https://devfeed.tech/articles/deepseek-v4-a-million-token-context-that-agents-can-actually-use-7162.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/deepseekv4>)

Author: ben burtenshaw

Published: 2026-04-24T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [deepseek](<https://devfeed.tech/topics/deepseek.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [context window](<https://devfeed.tech/topics/context-window.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Cache](<https://devfeed.tech/topics/cache.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agents](<https://devfeed.tech/tags/agents.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [cache](<https://devfeed.tech/tags/cache.md>), [community](<https://devfeed.tech/tags/community.md>), [context-window](<https://devfeed.tech/tags/context-window.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm](<https://devfeed.tech/tags/llm.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [moe](<https://devfeed.tech/tags/moe.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [research](<https://devfeed.tech/tags/research.md>)

## AI overview

DeepSeek-V4 is presented as an open model designed for long-running agentic workloads. The article explains how its architecture reduces the inference FLOPs and KV cache memory required for million-token contexts, and describes post-training choices intended to improve extended tool-use trajectories. It highlights Compressed Sparse Attention and a lightning indexer as mechanisms behind the efficiency gains, with comparisons against DeepSeek-V3.2 and grouped-query attention.

## Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.