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

The shift is not in token capacity, but in context injection reliability. DeepSeek-V4 demonstrates that massive context (1M tokens) reduces hallucinations in complex agent planning, improving RAG quality beyond simple chunking.

This change is meaningless if inference costs spike. Deploying a 1M context window using GPT-4 costs $12.50 per 1000 tokens, which makes high-volume agent workflows economically infeasible for most teams.

Agents and RAG teams must prioritize system design over raw context size. Deploying fine-tuned Haiku models for context retrieval minimizes inference costs by 40% compared to GPT-4 for this specific use case.

Do not blindly inflate context size; instead, implement strict context compression pipelines using Claude 3 for pre-filtering before feeding data to the agent system.