deepseek-ai/DeepSeek-V4-Pro-0813 · Hugging Face

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DeepSeek-V4-Pro-0813

Technical Report 👁️

Introduction

DeepSeek-V4-Pro-0813 is the official release of DeepSeek-V4-Pro , superseding the preview version, with greatly enhanced agentic capabilities and performance improvements that are especially pronounced in production environments. It is built on the DeepSeek-V4-Pro (Preview) model structure, with a DSpark speculative decoding module attached.

DeepSeek-V4-Pro-0813 outperforms DeepSeek-V4-Pro (Preview) on the benchmarks listed below, and is broadly competitive with the strongest proprietary models available.

Benchmark<br>DeepSeek-V4-Pro-0813<br>DeepSeek-V4-Flash-0731<br>DeepSeek-V4-Pro (Preview)<br>DeepSeek-V4-Flash (Preview)<br>GLM-5.2<br>Kimi K3<br>Opus-4.8<br>Fable-5 (w/ fallback)

HLE (wo / w tools)<br>42.7 / 60.0<br>37.8 / 51.5<br>37.7 / 48.2<br>34.8 / 45.1<br>40.5 / 54.7<br>43.5 / 56.0<br>49.8 / 57.9<br>53.3 / 63.0

Terminal Bench 2.1<br>87.9<br>82.7<br>72.1<br>61.8<br>81.0<br>88.3<br>85.0<br>88.0

NL2Repo<br>61.5<br>54.2<br>38.5<br>39.4<br>48.9<br>69.7

Cybergym<br>83.3<br>76.7<br>52.7<br>38.7<br>80.0<br>78.3<br>83.1

DeepSWE<br>62.7<br>54.4<br>12.8<br>7.3<br>46.2<br>67.5<br>58.0<br>70.0

Toolathlon-Verified<br>74.1<br>70.3<br>55.9<br>49.7<br>59.9<br>76.5<br>76.2<br>77.9

Agents' Last Exam<br>25.7<br>25.2<br>16.5<br>15.8<br>23.8<br>27.6<br>25.7

AutomationBench (Public)<br>31.8<br>25.1<br>12.8<br>10.8<br>12.9<br>30.8<br>27.2<br>29.1

DSBench-FullStack †<br>71.1<br>68.7<br>41.8<br>37.0<br>61.8<br>73.7<br>71.6<br>77.2

DSBench-Hard †<br>67.2<br>59.6<br>31.1<br>25.8<br>54.5<br>63.0<br>71.7<br>68.3

Notes:

For the code-agent tasks among the public benchmarks above, DeepSeek-V4-Pro-0813 is 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.

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

Chat Template

This release does not include a Jinja-format chat template. Instead, we provide a dedicated encoding folder with Python scripts and test cases demonstrating how to encode messages in OpenAI-compatible format into input strings for the model, and how to parse the model's text output. Please refer to the encoding folder for full documentation.

The reasoning_effort parameter now supports three levels — low, high, and max — which control how much deliberation the model spends before answering.

A brief example:

from encoding_dsv4 import encode_messages, parse_message_from_completion_text

messages = [<br>{"role": "user", "content": "hello"},<br>{"role":...