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LatestDeepSeekV4-Flash-Vision-Exp
DeepSeekVision-Language

DeepSeek adds vision to its V4 Flash line

An experimental, MIT-licensed vision-language model brings image understanding to DeepSeek's fast V4 Flash architecture.

Aug 31, 2026
NotableMIT
DeepSeek-V4-Flash-Vision-Exp

DeepSeek has published DeepSeek-V4-Flash-Vision-Exp, an experimental vision-language variant of its V4 Flash model. As the name signals, this is a work-in-progress release rather than a polished flagship, but it extends the company's fast, lightweight line into multimodal territory.

The model handles both image and text inputs, pairing visual understanding with the text generation of the underlying Flash architecture. Like other recent DeepSeek releases, it uses a mixture-of-experts design, which activates only part of the network per token to keep inference costs down while preserving capacity.

Why it matters

DeepSeek has built a reputation for shipping capable models under permissive terms, and this release continues that pattern:

  • It carries an MIT license, allowing broad commercial and research use.
  • It targets the Flash tier, oriented toward efficiency rather than maximum scale.
  • It marks the family's first step into vision-language tasks.

The "Exp" tag is a clear caution: DeepSeek is signaling that behavior, capabilities, and stability may shift before any production-grade version arrives. For developers, that makes this a model to experiment with rather than deploy, but it offers an early look at how DeepSeek intends to fold vision into its faster models. Details on parameter counts, context length, and benchmarks were not specified in the release; those interested should consult the model card directly.

Sources

  • deepseek-ai/DeepSeek-V4-Flash-Vision-Exp

    Hugging Face

    Visit
OlderH Company's NeoMME rethinks visual document retrievalH company · Embeddings · 2 months agoNewerViggle Releases Viggle-Animate for Character SwapsViggle · Image → Video · last month

Get the model

Hugging Face

Specs

Context window1.0M tokens
Size306.7 GB
PrecisionINT8
ArchitectureDeepseekV4ForCausalLM
LicenseMIT
Downloads508.5K
Likes951

Can you run it?

Needs a multi-GPU server — about 184.7 GB at 4-bit.

  • FP8 (as published)

    4×H100 80 GB · 512 GB Mac Studio

    308.4 GB
  • 4-bit

    4×H100 80 GB · 512 GB Mac Studio

    184.7 GB
Your machine
GGUF builds
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLMVision-Language

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