Report on the performance and evaluation of DeepSeek-V4-Flash-Vision-Exp

1. Summary

DeepSeek-V4-Flash-Vision-Exp is an experimental multimodal model that incorporates a vision module into the DeepSeek-V4-Flash architecture. According to the Hugging Face Model Card, multimodal agent capabilities have been significantly improved compared to existing models.

The vision capabilities of this model are designed with a focus on task execution for agents. It is capable of directly reading visual information such as charts, software interfaces, and web screenshots. Performance on text tasks is reported to be equivalent to conventional models.

2. Bunrin Bench (BUNRIN LABO Original Test)

No data available (scheduled). For the methodology, refer to About Bunrin Bench.

3. Various Benchmarks

Based on figures from the Hugging Face Model Card.

| Benchmark | V4-Flash-Vision-Exp | V4-Flash-0731 | Opus-4.8 |
| :--- | :---: | :---: | :---: |
| Text Agent | | |
| 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 | | |
| 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 |

† In ApexBench and Agents' Last Exam, V4-Flash-0731 was evaluated by ignoring multimodal elements within the input.

4. Official announcements

2026-09-05: llama.cpp announced support for Qwen3.8-Flash-Next and Nemotron-3-Puzzle, as well as the addition of video input options.

2026-09-02: FlashLabs Inc. reported that it has released "DeepSeek-V4-Flash-Vision-Uncensored," a GFUQquantized build with relaxed content filtering.

2026-08-31: Model weights were uploaded to Hugging Face.

2026-08-21: DeepSeek released deepseek-v4-flash-vision-exp in preview on its API platform.

5. Real-world performance (Community reception)

A user on Reddit r/LocalLLaMA reported operation in an environment using two RTX 6000 GPUs.

antirez demonstrated the model running at high speeds on a Mac M5 Max. The operation is fast.

Image inputs are compressed to 800x800 pixels. Consequently, there are reports that it is not suitable for reading fine details.

The latest version of LM Studio supports speculative decoding methods such as DSpark. This is said to reduce generation latency.

6. Recommended parameters

According to annotations in the Hugging Face Model Card:

  • temperature = 1.0
  • top_p = 0.95
  • reasoning effort: max
  • Agent framework: DeepSeek Harness (minimal mode)

7. Sources