Read our How to Run DeepSeek-V4-0731 Guide!

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

To run DeepSeek-V4-Flash-0731 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-0731 in Unsloth Studio with toggles for High and Max thinking.

New DSpark support allowing up to 2x faster decoding! Docs for DSpark

DeepSeek-V4-Flash-0731

Technical Report👁️

Introduction

DeepSeek-V4-Flash-0731 is the official release of DeepSeek-V4-Flash, superseding the preview version, with substantially enhanced agentic capabilities. It has the same model structure as DeepSeek-V4-Flash-DSpark, i.e. it comes with a speculative decoding module attached.

DeepSeek-V4-Flash-0731 outperforms DeepSeek-V4-Pro (Preview) on benchmarks listed below despite its far smaller activated parameter count, and is broadly competitive with the strongest proprietary models available.

| :--- | :---: | :---: | :---: | :---: | :---: |

| Terminal Bench 2.1 | 82.7 | 61.8 | 72.1 | 81.0 | 85.0 |

| NL2Repo | 54.2 | 39.4 | 38.5 | 48.9 | 69.7 |

| Cybergym | 76.7 | 38.7 | 52.7 | - | 83.1 |

| DeepSWE | 54.4 | 7.3 | 12.8 | 46.2 | 58.0 |

| Toolathlon-Verified | 70.3 | 49.7 | 55.9 | 59.9 | 76.2 |

| Agents' Last Exam | 25.2 | 15.8 | 16.5 | 23.8 | 25.7 |

| AutomationBench Public | 25.1 | 10.8 | 12.8 | 12.9 | 27.2 |

| DSBench-FullStack † | 68.7 | 37.0 | 41.8 | 61.8 | 71.6 |

| DSBench-Hard † | 59.6 | 25.8 | 31.1 | 54.5 | 71.7 |

Notes:

1. For the Code Agent tasks among the public benchmarks above, DeepSeek-V4-Flash-0731 is evaluated with the minimal mode of DeepSeek Harness (to be released) as the agent framework, using the max reasoning effort level with temperature = 1.0, top_p = 0.95.

2. † DSBench-FullStack is an internal full-stack development test set; DSBench-Hard is an internal test set of difficult coding-agent problems.

License

This repository and the model weights are licensed under the MIT License.

Citation

@misc{deepseekai2026deepseekv4,
      title={DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence},
      author={DeepSeek-AI},
      year={2026},
}