Read our How to Run DeepSeek-V4 Guide!

Unsloth Dynamic 3.0 achieves superior accuracy & outperforms other leading quants.

• To run DeepSeek-V4-Flash-Vision-Exp in full precision lossless, run Q8 (UD-Q8_K_XL), which is 162GB and only 7GB bigger than Q4 (UD-Q4_K_XL).
• See our DeepSeek-V4 guide for quantization analysis and instructions.
• You can now run DeepSeek-V4-Flash-Vision-Exp in Unsloth Studio with toggles for High and Max thinking.


DeepSeek-V4-Flash-Vision-Exp

Image input requires llama.cpp b10766 or later, which is the first release containing the DeepSeek-V4 vision support from #28133 and #28154.



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Introduction

We are excited to introduce DeepSeek-V4-Flash-Vision-Exp, our first experimental multimodal model in the DeepSeek-V4 family. It builds on the DeepSeek-V4-Flash architecture by incorporating visual modules and undergoing continued training to unlock visual understanding capabilities.

Compared to DeepSeek-V4-Flash-0731, DeepSeek-V4-Flash-Vision-Exp achieves substantial improvements on its multimodal agent capabilities, while maintaining comparable performance on text-only agent tasks.

Benchmark	DeepSeek-V4-Flash-Vision-Exp	DeepSeek-V4-Flash-0731	Opus-4.8
Text Agent Capabilities
Terminal Bench 2.1	83.9	82.7	85.0
NL2Repo	57.7	54.2	69.7
Cybergym	75.3	76.7	78.3
DeepSWE	59.3	54.4	58.0
Toolathlon-Verified	75.9	70.3	76.2
DSBench-Hard	63.6	59.6	71.7
AutomationBench (Public)	25.7	25.1	27.2
Multimodal Agent Capabilities
ApexBench (Pass@1)	36.5	26.2†	39.4
Agents' Last Exam	27.3	25.2†	25.7
Chartography	64.3	-	65.0
ZeroBench (Pass@5)	35.0	-	34.0

Notes:

1. For the text agent benchmarks above, DeepSeek models are evaluated with the minimal mode of DeepSeek Harness as the agent framework, using the max reasoning effort level with temperature = 1.0, top_p = 0.95.
2. † For ApexBench and Agents' Last Exam, DeepSeek-V4-Flash-0731 ignores the multimodal elements in the input.

Repository layout

This repository contains the tokenizer, prompt encoding reference, and a minimal PyTorch inference implementation for DeepSeek-V4 Flash Vision. The reference inference covers the vision encoder and aligner, DFlash attention, MoE, Hyper-Connections, and the DSpark forward path.

.
├── encoding/                  # OpenAI-style messages -> model prompt
├── inference/                 # weight conversion and minimal inference
│   └── examples/              # equivalent TXT and JSON vision prompts
├── config.json                # Hugging Face model metadata
├── generation_config.json
├── model.safetensors.index.json
├── tokenizer.json
└── tokenizer_config.json

encoding/ and inference/ deliberately remain separate: prompt formatting does not depend on PyTorch, while inference imports the sibling encoding module with an explicit Python path. No symlinks are required.

The tokenizer files are regular files so that the repository can be uploaded to Hugging Face without relying on local filesystem symlinks. The large model shards are described by model.safetensors.index.json and are not duplicated inside the source checkout used to assemble this repository.

Prompt encoding

See encoding/README.md. Both OpenAI-style JSON content blocks and the compact <image>path</image> TXT notation are supported. The two examples under inference/examples/ encode to identical prompts and token IDs.

Minimal inference

See inference/README.md for dependency installation, checkpoint conversion, and TXT/JSON inference commands.

License

This repository is licensed under the MIT License.