DeepSeek 1M Context Window: Strategies & Caching

DeepSeek V4 made 1M token context the default across all services — not a premium tier, not a special flag, just the standard. At 5x Claude's 200K context, this removes entirely different categories of engineering constraints. You can load 10 full novels, an entire monorepo, or a year of customer conversations into a single prompt.

But 1M context creates new challenges. Context caching — DeepSeek's automatic, disk-based KV cache layer — makes repeated prompts against static content dramatically cheaper, but only if your prompts are structured for prefix matches. The pages in this section cover both the ambitious scale of 1M prompting and the practical economics of making it cost-effective.

What You'll Find Here

1M Context Strategies

Prompt structuring for the 1M window. How 5x Claude's context changes retrieval economics, document loading strategies, and attention management. The U-shape attention curve at megabyte scale.

Context Caching

DeepSeek's automatic disk-based KV cache. How prefix-exact-match caching works, designing cache-aware prompts that unlock 50x cost reduction, and understanding when caches hit vs miss.

Needle-in-Megahaystack

Retrieval patterns at 1M scale. When the full window is worth loading vs RAG. Multi-hop question answering across megabyte-scale documents. Verification strategies for 1M context retrieval.