DeepSeek-V4.1-Flash<\/strong> is a large MoE model from DeepSeek. It has 552B backbone parameters<\/strong> and activates 8B\u201316B per token<\/strong>. The model card lists additional components such as “Engram” memory (196B parameters, looked up sparsely depending on the token), and Hugging Face lists 763.2B parameters in total. It supports contexts of up to one million tokens<\/strong> and is natively multimodal<\/strong>, having learned images and text together from the start of pre-training. It was trained from scratch on 45T tokens.<\/p>\n It also lets you set reasoning effort as an integer from 1 to 100, so you can trade speed and cost against accuracy for each use case.<\/p>\n 1. It uses far less memory for long contexts.<\/strong> The memory needed to hold the context (the KV cache) is 890 bytes per token<\/strong>\u2014about 1\/4 of the previous DeepSeek-V4-Flash and about 1\/437 of the first DeepSeek model. This comes from a new attention design (CSA2) that shares and reuses KV across layers, combined with storing the KV cache itself in 4 bits. It is aimed at handling million-token contexts with practical amounts of memory, which is why the model card is subtitled “Pushing the Limits of KV Cache Compression.”<\/p>\n 2. On agentic tasks it stands alongside closed frontier models.<\/strong> An excerpt from the publisher’s comparison, at maximum reasoning effort:<\/p>\n \u2192 Scroll horizontally to see all columns<\/p>\nWhat Makes It Stand Out<\/h2>\n