# DeepSeek Releases V4.1-Flash, an Open Multimodal MoE

> The new mixture-of-experts model handles text, vision, and reasoning, and ships under a permissive MIT license.

Published by The Open Weights on Oct 17, 2026. Canonical: https://theopenweights.com/news/deepseek-v4-1-flash-rhkb

## Key facts

- Company: DeepSeek
- Model: DeepSeek-V4.1-Flash
- Version: V4.1-Flash
- Category: Text / LLM
- Modalities: Text / LLM, Reasoning, Vision-Language
- License: MIT (Open weights, commercial use allowed)
- Context window: 1.0M tokens
- Architecture: DeepseekV41ForCausalLM
- Precision: INT8
- Weights size: 765.3 GB
- Significance: major
- Published: 2026-10-17
- Last verified: 2026-11-08
- Hugging Face: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017
- Canonical URL: https://theopenweights.com/news/deepseek-v4-1-flash-rhkb

DeepSeek has published **DeepSeek-V4.1-Flash**, a mixture-of-experts (MoE) model that combines text, vision, and reasoning capabilities in a single release. The weights are available now on [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017) under an MIT license, one of the most permissive terms a developer can adopt.

The "Flash" naming signals DeepSeek's intent to position this as a faster, more efficient member of the V4.1 line. As an MoE design, the model routes each input through a subset of specialized expert networks rather than activating every parameter, a approach that keeps inference costs lower while preserving capacity. DeepSeek has not published a full parameter count or context window alongside this release.

## Why it matters

Multimodal models that are genuinely open remain relatively scarce, and an MIT license removes most of the friction that holds teams back from building on or fine-tuning a model commercially. The combination here is notable:

- Text, vision (VLM), and reasoning in one checkpoint
- MoE architecture aimed at efficient inference
- Fully permissive MIT terms

DeepSeek has built a reputation for shipping competitive open models at a pace that pressures both open and closed competitors, and V4.1-Flash extends that strategy into multimodal territory. Developers evaluating the release will want to confirm context length, active-parameter counts, and benchmark figures as more documentation lands on the model's [Hugging Face page](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017).

## Get the model

- [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017)

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Source: The Open Weights (https://theopenweights.com/). Aggregated and written by Claude, curated by humans. Cite as: "DeepSeek Releases V4.1-Flash, an Open Multimodal MoE", The Open Weights, Oct 17, 2026, https://theopenweights.com/news/deepseek-v4-1-flash-rhkb