Quick answer
DeepSeek published the weights, tokenizer, prompt encoder, and minimal inference code for DeepSeek-V4-Flash-Vision-Exp on Hugging Face on August 31, 2026. The experimental model adds image understanding to the V4 Flash architecture and is licensed under MIT. Its configuration declares a 1,048,576-position context and a 32-layer vision encoder. The exact model was already available through DeepSeek's API from August 21, but local deployment now has an official weight repository. DeepSeek's benchmark comparisons are first-party results, and availability in Chat AI has not been verified.
DeepSeek has released the experimental vision model's weights
The official Hugging Face repository contains the model weights, tokenizer, prompt-encoding reference, configuration, and minimal PyTorch inference code for DeepSeek-V4-Flash-Vision-Exp. DeepSeek describes it as the first experimental multimodal model in the V4 family. It builds on V4 Flash with visual modules and continued training for image understanding while retaining text and agent capabilities.
Sources: DeepSeek on Hugging Face
The model accepts images alongside text
DeepSeek's API documentation lists JPEG, PNG, GIF, and WebP inputs. Applications can send an image as base64 data, a public URL, or a file previously uploaded through the Files API. DeepSeek says the model can describe images, read screenshots, and analyze charts. Image inputs are converted to tokens based on their dimensions and billed together with text when the hosted API is used.
The configuration exposes a one-million-token context and a dedicated vision encoder
The published configuration sets max_position_embeddings to 1,048,576. It also describes a 32-layer vision encoder with a 1,024-dimensional hidden representation, 16 attention heads, and a maximum of 384 vision tokens. Those values document the checkpoint's configuration; they do not guarantee that every serving stack, request type, or hardware setup can use the full context efficiently.
Sources: DeepSeek on Hugging Face
The repository includes a local inference path, but deployment is not lightweight
DeepSeek's repository includes prompt encoders for OpenAI-style message blocks and compact image-path notation, plus a minimal inference implementation covering the vision encoder, aligner, mixture-of-experts backbone, DFlash attention, Hyper-Connections, and DSpark forward path. The Hugging Face repository is roughly 168 GB and contains dozens of weight shards, so a developer should check storage, accelerator memory, runtime compatibility, and supported quantization before treating this as a local-laptop model.
Sources: DeepSeek on Hugging Face
The weights and reference code use the MIT license
The repository is published under the MIT license, which permits use, modification, distribution, sublicensing, and sale subject to preserving the required notice and license terms. That broad software license does not remove obligations that may apply to input data, generated output, privacy, safety, or downstream product use.
Sources: DeepSeek on Hugging Face
DeepSeek's benchmark table is useful context, not independent verification
DeepSeek reports that the vision checkpoint improves on V4 Flash in its selected multimodal agent evaluations while remaining comparable on text-only agent tasks. The model card documents the evaluation settings and comparison table, but these are provider-run results. Teams should test image reading, chart interpretation, tool use, accuracy, latency, and operating cost on their own workloads before choosing the model.
Sources: DeepSeek on Hugging Face, DeepSeek
Availability in Chat AI has not been verified
DeepSeek-V4-Flash-Vision-Exp is a distinct experimental checkpoint. The exact model does not appear in Chat AI's currently verified model directory, so this article does not claim that it is available in Chat AI. Model access can also change across the DeepSeek API, self-hosted deployments, and third-party platforms.
Sources: Chat AI, DeepSeek on Hugging Face
Frequently asked questions
What readers usually ask
What is DeepSeek-V4-Flash-Vision-Exp?
It is DeepSeek's first experimental multimodal model in the V4 family. It combines the V4 Flash language-model architecture with visual modules so it can process images alongside text.
Are the DeepSeek V4 Flash Vision weights available to download?
Yes. DeepSeek has published model weights, configuration, tokenizer files, prompt encoding, and minimal inference code in its official Hugging Face repository.
What license does the model use?
The official repository uses the MIT license. Developers should read the license and separately review obligations for data, privacy, safety, and downstream use.
Which image formats does the DeepSeek API accept?
DeepSeek documents JPEG, PNG, GIF, and WebP. Images can be supplied as base64 data, a public URL, or a Files API reference.
Does the model support a one-million-token context?
Its published configuration declares 1,048,576 maximum positions. Effective usable context and performance can still depend on the serving stack, hardware, request shape, and implementation.
Is DeepSeek-V4-Flash-Vision-Exp available in Chat AI?
Availability has not been verified. The exact experimental model is not listed in Chat AI's current verified model directory.
Evidence
Sources
- DeepSeek-V4-Flash-Vision-Exp model cardDeepSeek on Hugging Face · Primary source
- DeepSeek-V4-Flash-Vision-Exp API releaseDeepSeek · Primary source
- DeepSeek Vision API guideDeepSeek · Primary source
- DeepSeek-V4-Flash-Vision-Exp configurationDeepSeek on Hugging Face · Primary source
- DeepSeek-V4-Flash-Vision-Exp licenseDeepSeek on Hugging Face · Primary source
- Chat AI model directoryChat AI · Primary source