deepseek-v4.1-flash

DeepSeek-V4.1-Flash is a next-generation open-source large language model developed by DeepSeek, designed for high concurrency, complex long-range intelligent agents, and engineering-grade code delivery—boasting "leapfrogging intelligence + ultra-high throughput at lightning speed." The model adopts a 552B backbone MoE architecture and integrates a 196B Engram module (only 8B are activated during prefill, while 16B are activated during decode), achieving a breakthrough leap in inference throughput (up to over 400 TPS) and first-token latency. In multiple rigorous real-world scenario tests, V4.1-Flash has comprehensively outperformed its predecessor, V4 Pro, in both overall reasoning quality and the speed of completing complex workflows, making it an absolute cost-effective powerhouse for enterprises scaling AI code assistants, intelligent agent clusters, and high-density workflows.

───────────────────────────────────────────────────────────────────

Core Capabilities

Leapfrogging Intelligence and Full-Scale Routing Replacement: In both internal and external testing, V4.1-Flash’s overall performance, generation speed, and task completion rate have significantly surpassed those of its flagship predecessor, V4 Pro.

Engram Architecture and Extreme KV Cache Compression: By combining the 196B Engram embedding module with a brand-new QAT compression algorithm, the memory footprint of the KV cache for ultra-long contexts of 1M tokens has been dramatically reduced to approximately 900MB, delivering an exceptional experience for long-text prefill and continuous follow-up queries.

Ultra-High Throughput (Up to 400+ TPS): End-to-end inference speed has been greatly improved, with extremely fast first-token response times, making it ideal for high-concurrency API access and real-time interactive systems.

Ultra-Long 1M Token Context and Automatic Seamless Caching: Natively supports a 1,000,000 (1M) token window and exports up to 384K tokens without requiring manual configuration. It also natively supports disk-level context caching with automatic hit detection.