DeepSeek-V4-Flash-Vision-Exp
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:
- For the text agent benchmarks above, DeepSeek models are evaluated with the minimal mode of DeepSeek Harness as the agent framework, using the
maxreasoning effort level withtemperature = 1.0, top_p = 0.95. - † 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.