DeepSeek V4.1 Flash 1017 specs: 552B MoE, 1M context, KV footprint cut to a quarter
Spec rundown: 1017 is a Pareto update beating GLM 5.3 and Kimi K3 on every benchmark, with a 552B multimodal MoE, 1M context, adjustable reasoning effort and ~890 bytes/token KV.
DeepSeek describes 1017 as a Pareto update: stronger benchmarks, faster task completion, and shorter outputs, with reinforcement learning from self-feedback aimed at complete project generation. Surpassing GLM 5.3 + Kimi K3 on every benchmark 👀 It is live in the app/API and as downloadable weights; @deepseek_ai specifically recommends Harness v0.2.1-rc.1 or newer, which matters because agent scores are increasingly harness-dependent rather than model-only properties. The underlying V4.1-Flash architecture is a 552B-backbone multimodal MoE with 8B parameters active in prefill, 16B in decode, a 1M-token context, and continuously adjustable reasoning effort from 1–100. Its Causal Encoder-Decoder design plus bounded SWA replay and FP4 KV caching reduces persistent KV footprint to about 890 bytes/token—roughly one quarter of V4-Flash—making long-context agent workloads the more interesting claim than headline parameter count. Go download & install the new 1017 update Do you think it’s benchmaxed or will it hold up against open-sourced models with its intelligence? 🧐
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