DeepSeek released DeepSeek-V4-Flash-Vision-Exp, its first experimental multimodal model in the V4 family
DeepSeek released DeepSeek-V4-Flash-Vision-Exp on Hugging Face under the MIT license. It is an image-text-to-text model built on DeepSeek-V4-Flash with added visual modules. Compared to DeepSeek-V4-Flash-0731, it improves multimodal agent benchmarks: ApexBench (Pass@1) 36.5 vs 26.2, Agents' Last Exam 27.3 vs 25.2, while text-only agent performance remains comparable (Terminal Bench 2.1 83.9 vs 82.7).
China context
- Original name
- 深度求索
- Outside China
- Open weights · huggingface.co
- Claims
- Company-reported; not yet independently evaluated
- For builders
- Developers outside China can download the model weights from Hugging Face and integrate multimodal capabilities under the MIT license.
- For investors
- The release signals DeepSeek's continued investment in open multimodal models, which may pressure other open-weight competitors.
DeepSeek released DeepSeek-V4-Flash-Vision-Exp, an experimental multimodal model based on DeepSeek-V4-Flash. The model adds visual understanding capabilities and shows improved performance on multimodal agent benchmarks while maintaining text-only agent performance. The repository includes tokenizer, prompt encoding reference, and a minimal PyTorch inference implementation, with support for vLLM and SGLang.
The model incorporates a vision encoder and aligner, DFlash attention, MoE, Hyper-Connections, and a DSpark forward path. It supports OpenAI-style JSON content blocks and compact <image path</image> TXT notation for prompt encoding. Inference can be run on a single 4×GB300 node with vLLM, and SGLang supports speculative decoding via DSpark.
DeepSeek is extending its open-weights model family into multimodal capabilities, directly competing with other open multimodal models. The MIT license allows broad commercial use, which may accelerate adoption among developers and enterprises.
The MIT license and availability on Hugging Face lower barriers for developers to integrate multimodal capabilities into applications. The model's improved multimodal agent performance could be valuable for tasks involving visual inputs, such as UI automation or document understanding.
Observable next signals include whether DeepSeek releases a non-experimental multimodal model, whether the model is integrated into DeepSeek's API or chat platform, and how the community benchmarks it against other open multimodal models.