🤖DeepSeek V4.1 Flash 1017: 552B-Parameter Model Released
New cost-efficient AI model for long-horizon tasks
TL;DR
DeepSeek released V4.1 Flash 1017, a 552B-parameter model with native image understanding and KV caching. It's designed for long-horizon tasks and coding agents, offering better performance than V4 Pro at a lower cost.
DeepSeek just dropped V4.1 Flash 1017, a 552B-parameter model with native image understanding and KV caching. This model is a game-changer for teams running long-horizon tasks and coding agents. It outperforms V4 Pro in performance, speed, and task completion time, all while being more cost-efficient. Key details: 16B output parameters, $0.15 input and $0.60 output pricing per 1M, and 1.24s latency. The model's availability is 99.72%, and it's the first built on the company's Causal Encoder-Decoder (CED) architecture.
Key Points
V4.1 Flash 1017 has 552B parameters and activates 8B on input, 16B on output.
Model exceeds V4 Pro in performance, speed, and task completion time.
Pricing: $0.15 input, $0.60 output per 1M tokens, with 1.24s latency.
First model built on DeepSeek's Causal Encoder-Decoder (CED) architecture.
Native image understanding and KV caching reduce cache memory to a quarter.
Why It Matters
If you're running long-horizon tasks or coding agents, DeepSeek V4.1 Flash 1017 is a must-look. It outperforms V4 Pro while being more cost-efficient, thanks to native image understanding and KV caching. The model's $0.15 input and $0.60 output pricing per 1M tokens makes it a strong choice for teams with tight budgets.
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