DeepSeek has just released a paper describing its latest model, DeepSeek V4.1 Flash. This multimodal mixture-of-experts system packs 552B parameters, can handle up to 1 million tokens of context, and was trained on a staggering 45,000 billion tokens. One few notable points are highlighted in the announcement.
The architecture introduces a Causal Encoder-Decoder (CED) design that activates only 8B parameters during the input (prefill) stage and 16B when generating output (decode). This selective activation cuts operational costs dramatically for agent-type workloads. In addition, the model employs a compressed KV cache strategy that merges Compressed Sparse Attention 2 (CSA2) with FP4 KV caching, allowing the cache size to shrink to just 890 bytes per token – roughly one-quarter of the previous DeepSeek V4-Flash and a 437-fold reduction compared with DeepSeek V1.
Even with the reduced memory footprint, DeepSeek V4.1 Flash outperforms on several agent benchmarks, including Terminal-Bench 3.0, DeepSWE, CyberGym, and Automatización-Bench, positioning it as a strong competitor to Opus5 and GPT5.6-Sol. The model is already available on Hugging Face for immediate experimentation.
Conclusion
DeepSeek V4.1 Flash demonstrates that high-end performance can be achieved without the typical resource overhead, making it an attractive option for developers seeking powerful yet efficient AI capabilities.
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