NVIDIA Build lists DeepSeek-V4-Flash-0731 MoE model

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DeepSeek-V4-Flash-0731 is a sparse Mixture-of-Experts language model featuring 13B active parameters and a 1-million-token context window. The NVIDIA Build model card outlines technical specifications, architecture details, and coding benchmark performances.

Key points

  1. Sparse MoE architecture featuring 13 billion active parameters (the document lists 284 billion total parameters in specs and 304 billion in the architecture section).
  2. Supports an input context length of 1,048,576 tokens (1M tokens).
  3. Incorporates Compressed Sparse Attention, Heavily Compressed Attention, Manifold-Constrained Hyper-Connections, and speculative decoding.
  4. Provides native support for function calling, structured outputs, and tiered reasoning effort levels (low, high, max).
  5. Pretrained on over 32 trillion tokens, with post-training targeted at reasoning and agent capabilities.
  6. Runs on runtime engines such as vLLM and SGLang across NVIDIA Hopper (H100, H200) and Blackwell hardware.

Why it matters

MoE architectures that activate only a small slice of total parameters offer high reasoning capacity and huge context windows without the proportional compute cost of dense models. Standardised serving via vLLM and API endpoints streamlines adoption in developer workflows.

Analysis

DeepSeek-V4-Flash-0731 is positioned directly for programming, agentic execution, and long-context processing, reporting solid results on benchmarks like Terminal Bench 2.1 (82.7) and Toolathlon-Verified (70.3). Minor inconsistencies in the model card regarding total parameter counts (284B vs. 304B) appear in the source. Its compatibility with standard OpenAI API formatting and LangChain simplifies drop-in replacement across agent tooling.

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Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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