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LatestDeepSeekV4.1-Flash-1017
DeepSeekVision-Language

DeepSeek V4.1-Flash Adds Vision and Reasoning

The new MIT-licensed checkpoint folds image understanding and reasoning into DeepSeek's fast, mixture-of-experts Flash line.

Oct 17, 2026
Major releaseMIT
DeepSeek-V4.1-Flash (1017)

DeepSeek has published a new checkpoint in its Flash family, DeepSeek-V4.1-Flash-1017, on Hugging Face. The model combines vision-language understanding with text generation and reasoning, and it ships under a permissive MIT license that allows commercial use.

The Flash branding signals DeepSeek's lighter, latency-focused tier, meant to run more efficiently than the company's flagship models while still covering a broad set of tasks. Under the hood it uses a mixture-of-experts (MoE) design, which activates only a subset of parameters per token to keep inference costs down.

Why it matters

Multimodal capability is quickly becoming table stakes for open-weight releases, and DeepSeek extending that into its fast Flash line lowers the barrier for developers who need image-aware reasoning without paying for the largest checkpoints.

  • Vision-language, text, and reasoning in a single model
  • Mixture-of-experts architecture for efficient inference
  • MIT license, permitting commercial deployment

DeepSeek has not published detailed specifications such as parameter counts or context length alongside the release, so independent evaluation will determine how the checkpoint stacks up against peers. For now, the weights are available directly from the project's Hugging Face repository.

Sources

  • deepseek-ai/DeepSeek-V4.1-Flash-1017

    Hugging Face

    Visit
OlderIBM's Granite Switch 4.1 Unifies Chat, Logic, SpeechIBM · Any-to-Any · 4 days ago

Get the model

Hugging Face

Specs

Context window1.0M tokens
Size765.3 GB
PrecisionINT8
ArchitectureDeepseekV41ForCausalLM
LicenseMIT
Downloads17.9K
Likes1.7K

Can you run it?

Datacenter only — about 459.5 GB even at 4-bit.

  • FP8 (as published)

    Multi-node cluster

    767 GB
  • 4-bit

    8×H100 80 GB node

    459.5 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 / LLMReasoningVision-Language

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