DeepSeek's new V4.1-Flash model cuts memory needs for AI agents

DeepSeek released V4.1-Flash, a 552-billion-parameter multimodal model that activates only 16 billion parameters per token and cuts key-value cache memory to a quarter of its predecessor. On the DeepSWE coding benchmark it narrowly beats Opus 5 and GPT-5.6 Sol, and it ships under the MIT license.

Why it matters: Lower KV-cache memory directly cuts the cost of running long-context agents, which is often the real deployment bottleneck rather than raw model quality. An MIT-licensed model beating closed frontier models on a coding benchmark keeps pricing and licensing pressure on Western labs.