AgentCPM-Explore-AWQ-8bit

Latest News

• [2026-01-12]🚀🚀🚀 We have open-sourced AgentCPM-Explore, an agent foundation model with only 4B parameters, together with its entire training and inference infrastructure. AgentCPM-Explore has successfully entered 8 classic long-horizon agent benchmarks, including GAIA,HLE, and BrowserComp. AgentCPM-Explore achieves SOTA performance at the same parameter scale and demonstrates its accurate deep research capabilities, effectively breaking the performance bottleneck for on-device agents.

Overview

Key highlights of AgentCPM-Explore include:

• The first full-parameter 4B agent model to rank on 8 long-horizon and complex agent benchmarks, including GAIA, HLE, and BrowserComp, in the on-device setting.

• Capable of over 100 rounds of continuous environment interaction, supporting multi-source information cross-validation, dynamic search strategy adjustment, and real-time verification of up-to-date information, enabling sustained deep exploration until task completion.

• Fully open-sourced end-to-end, including (1) AgentRL, a fully asynchronous reinforcement learning framework for agent training, (2) AgentDock, a unified management and scheduling platform for tool sandboxes, (3) AgentToLeaP, a one-click evaluation platform for agent tool-learning capabilities. These components collectively support community collaboration and custom extensibility.

We elaborate on the entire construction pipeline of AgentCPM-Explore on GitHub.

Experimental Results

<table> <thead> <tr> <th>Model</th> <th>GAIA (text-only)</th> <th>BrowseComp</th> <th>BrowseComp (ZH)</th> <th>HLE</th> <th>Frames</th> <th>WebWalker</th> <th>Seal-0</th> <th>Xbench-DeepSearch</th> </tr> </thead> <tbody> <tr> <td colspan="9"><strong>Closed-Source Models</strong></td> </tr> <tr> <td>Claude-4.5-sonnet</td> <td>71.2%</td> <td>19.6%</td> <td>40.8%</td> <td>24.5%</td> <td>85.0%</td> <td>/</td> <td>53.4%</td> <td>66.0%</td> </tr> <tr> <td>Gemini Deep Research</td> <td>/</td> <td>/</td> <td>/</td> <td>26.9%</td> <td>/</td> <td>/</td> <td>/</td> <td>/</td> </tr> <tr> <td>DeepSeek-V3.2</td> <td>63.5%</td> <td>67.6%</td> <td>65.0%</td> <td>40.8%</td> <td>80.2%</td> <td>/</td> <td>38.5%</td> <td>71.0%</td> </tr> <tr> <td>MiniMax-M2</td> <td>75.7%</td> <td>44.0%</td> <td>48.5%</td> <td>31.8%</td> <td>/</td> <td>/</td> <td>/</td> <td>72.0%</td> </tr> <tr> <td>OpenAI-GPT-5-high</td> <td>76.4%</td> <td>54.9%</td> <td>65.0%</td> <td>35.2%</td> <td>/</td> <td>/</td> <td>51.4%</td> <td>77.8%</td> </tr> <tr> <td>GLM-4.6</td> <td>71.9%</td> <td>45.1%</td> <td>49.5%</td> <td>30.4%</td> <td>/</td> <td>/</td> <td>/</td> <td>70.0%</td> </tr> <tr> <td>Kimi-Researcher</td> <td>/</td> <td>/</td> <td>/</td> <td>26.9%</td> <td>78.8%</td> <td>/</td> <td>36.0%</td> <td>69.0%</td> </tr> <tr> <td>Seed-1.8</td> <td>87.4%</td> <td>67.6%</td> <td>81.3%</td> <td>40.9%</td> <td>/</td> <td>/</td> <td>/</td> <td>/</td> </tr> <tr> <td colspan="9"><strong>Open-Source Models</strong></td> </tr> <tr> <td>MiroThinker 8B</td> <td>66.4%</td> <td>31.1%</td> <td>40.2%</td> <td>21.5%</td> <td>80.6%</td> <td>60.6%</td> <td>40.4%</td> <td>60.6%</td> </tr> <tr> <td>Tongyi DeepResearch 30B</td> <td>70.9%</td> <td>43.4%</td> <td>46.7%</td> <td>32.9%</td> <td>90.6%</td> <td>72.2%</td> <td>/</td> <td>75.0%</td> </tr> <tr> <td>ASearcher QWQ 32B v2</td> <td>58.7%</td> <td>/</td> <td>/</td> <td>/</td> <td>74.5%</td> <td>/</td> <td>/</td> <td>51.1%</td> </tr> <tr> <td>iterresearch-30B-A3B</td> <td>72.8%</td> <td>37.3%</td> <td>45.2%</td> <td>28.8%</td> <td>71.0%</td> <td>/</td> <td>39.6%</td> <td>/</td> </tr> <tr> <td>WebSailor-V2-30B-A3B (RL)</td> <td>74.1%</td> <td>35.3%</td> <td>44.1%</td> <td>30.6%</td> <td>/</td> <td>/</td> <td>/</td> <td>73.7%</td> </tr> <tr> <td>WebLeaper-30B-A3B-RUC</td> <td>73.2%</td> <td>38.8%</td> <td>/</td> <td>/</td> <td>/</td> <td>/</td> <td>48.6%</td> <td>72.0%</td> </tr> <tr> <td>WebDancer (QWQ-32B)</td> <td>51.5%</td> <td>3.8%</td> <td>18.0%</td> <td>/</td> <td>/</td> <td>47.9%</td> <td>/</td> <td>38.3%</td> </tr> <tr> <td>⭐ <strong>AgentCPM-Explore 4B</strong></td> <td>63.9%</td> <td>25.0%</td> <td>29.0%</td> <td>19.1%</td> <td>82.7%</td> <td>68.1%</td> <td>40.0%</td> <td>70.0%</td> </tr> </tbody> </table>